_ registry / mcp streamable-http · checked 4h ago

petri-pilot

https://pilot.pflow.xyz

Registry code: 87033a61e20ae4a9

api record
endpoint
https://pilot.pflow.xyz/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
391ms

last good check

priced tools
0

of 45 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 45 tools
45 never probed 0 of 45 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • petri_app_list unknown never probed

    List every named app in the app store, with the spec id each name currently points at.

    mcp-tool

    {
  "type": "object"
}
    arguments 3 lines
  • petri_dataset unknown never probed

    Generate a synthetic event log from a model: a seeded SSA playout, one case per arrival, emitted as CSV (case_id, activity, timestamp) — exactly the shape petri_conformance replays and the shape a calibration expects. Deterministic: same model, same seed, same bytes. Closes the loop generate → fit → conform without leaving the session.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "seed": {
      "type": "number",
      "description": "PRNG seed (default 1)"
    },
    "cases": {
      "type": "number",
      "description": "cases to generate (default 200, max 2000)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "format": {
      "type": "string",
      "description": "'csv' (default) or 'jsonl'"
    }
  }
}
    arguments 24 lines
  • petri_diff unknown never probed

    Compare two Petri net models and show structural differences. Reports added, removed, and modified places, transitions, arcs, roles, and access rules.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model_a",
    "model_b"
  ],
  "properties": {
    "model_a": {
      "type": "string",
      "description": "First model as JSON (the 'before' or 'base' model)"
    },
    "model_b": {
      "type": "string",
      "description": "Second model as JSON (the 'after' or 'new' model)"
    }
  }
}
    arguments 17 lines
  • petri_fit_discrete unknown never probed

    Fit transition rates to observed discrete event data — which transition fired at what time — by maximising the exact CTMC (Gillespie/SSA) log-likelihood. The stochastic counterpart of petri_fit: use it when the observations are individual firings or a recorded sample path rather than a smoothed (t, value) curve. Accepts the `paths` array petri_stochastic emits with record_events=true. Returns fitted rates, the per-transition firing counts, the closed-form MLE (count / exposure) alongside the optimiser's answer, and the negative log-likelihood before and after.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "paths"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "paths": {
      "type": "string",
      "description": "JSON array of observed sample paths: [{\"initial\": {place_id: count, ...}, \"horizon\": T, \"events\": [{\"time\": t, \"transition\": id, \"marking\": {place_id: count, ...}}, ...]}, ...]. \"marking\" is the post-firing marking and is optional (derived from stoichiometry when absent). Events must be time-ordered; horizon must be >= the last event time. Token places missing from \"initial\" default to the model's initial marking."
    },
    "verbose": {
      "type": "boolean",
      "description": "Include the CTMC likelihood derivation in the response. Default false"
    },
    "grad_tol": {
      "type": "number",
      "description": "Converged when max |∂(−log L)/∂rate| < grad_tol (default 1e-6)"
    },
    "max_iter": {
      "type": "number",
      "description": "Max Adam iterations (default 500, max 5000)"
    },
    "learn_rate": {
      "type": "number",
      "description": "Adam step size (default 0.05)"
    },
    "parameters": {
      "type": "string",
      "description": "Which rates to fit: a JSON array of transition ids, or an object {transition_id: initial_guess}. Default: every transition, starting from its model-declared rate (1.0 where none)"
    },
    "fixed_rates": {
      "type": "string",
      "description": "JSON object of rates for transitions NOT being fit (default: the model's declared rate, or 1.0)"
    }
  }
}
    arguments 41 lines
  • petri_heatmap unknown never probed

    Render the model's marking as a 2D colored grid heatmap (viridis colormap). Useful for high-place models (TTT boards, zk-ode topologies, grid-structured nets). Each cell shows place ID + value. Returns inline PNG.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "cols": {
      "type": "number",
      "description": "Grid columns (0/omit = auto-square)"
    },
    "rows": {
      "type": "number",
      "description": "Grid rows (0/omit = auto-square)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "title": {
      "type": "string",
      "description": "Optional title shown above the heatmap"
    },
    "labels": {
      "type": "boolean",
      "description": "Show place IDs and values in each cell (default true)"
    },
    "marking": {
      "type": "string",
      "description": "Optional JSON object {place_id: value} overriding the initial marking"
    }
  }
}
    arguments 32 lines
  • petri_lumping unknown never probed

    Proved place-level reductions of a model's mass-action ODE, each with the question it answers and the refusals it hit. Backward differential equivalence (Cardelli, Tribastone, Tschaikowski & Vandin, POPL 2016): the coarsest partition of places whose members hold identical trajectories for every uniform initial condition, in exact rational arithmetic. Constrained lumping (CLUE, Ovchinnikov et al. 2021), when 'observable' names places: the coarsest partition from which that sum alone stays exactly reconstructible — weaker, so it can find reductions the first cannot. Both refuse schedules and gates rather than answer a different net.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "observable": {
      "type": "string",
      "description": "Optional JSON array of place ids whose SUM is the observable for constrained lumping, e.g. [\"served\",\"vip_served\"]"
    }
  }
}
    arguments 16 lines
  • petri_ode_sweep unknown never probed

    Run multiple ODE trajectories at different rates and overlay them on one plot. Useful for seeing how dynamics change with a parameter — regime shifts, peak shifts, time-to-equilibrium. Each rate value gets its own viridis-colored line.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "transition",
    "observable"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "range": {
      "type": "string",
      "description": "JSON array [start, stop, n] generating n equally-spaced rates. Alternative to values"
    },
    "tspan": {
      "type": "string",
      "description": "Integration span (default [0, 10])"
    },
    "values": {
      "type": "string",
      "description": "JSON array of rate values to sweep. Alternative to range"
    },
    "samples": {
      "type": "number",
      "description": "Max samples per trajectory after downsampling (default 200)"
    },
    "observable": {
      "type": "string",
      "description": "Place ID whose trajectory is shown (one observable per call to keep the plot readable)"
    },
    "transition": {
      "type": "string",
      "description": "Transition ID whose rate is being swept"
    },
    "fixed_rates": {
      "type": "string",
      "description": "JSON object of other transition rates (default: the rate each transition declares in the model, 1.0 where none)"
    }
  }
}
    arguments 42 lines
  • petri_amm_depth unknown never probed

    Depth chart for a constant-product AMM: plots slippage (or output price) as a function of trade size. Use to answer 'how big can I trade before paying X% slippage?' and to size positions against pool depth.

    mcp-tool

    {
  "type": "object",
  "required": [
    "reserve_x",
    "reserve_y"
  ],
  "properties": {
    "fee_bps": {
      "type": "number",
      "description": "Pool fee in basis points (default 30)"
    },
    "reserve_x": {
      "type": "number",
      "description": "Reserve of token X (input)"
    },
    "reserve_y": {
      "type": "number",
      "description": "Reserve of token Y (output)"
    },
    "size_range": {
      "type": "string",
      "description": "JSON array [min_pct, max_pct, n] where pcts are fractions of reserve_x. Default [0.001, 0.5, 80]"
    },
    "trade_sizes": {
      "type": "string",
      "description": "JSON array of trade sizes (in X). Alternative to size_range"
    }
  }
}
    arguments 29 lines
  • petri_amm_il unknown never probed

    Impermanent loss curve for a Uniswap V2-style LP. Plots IL(r) = 2·√r/(1+r) − 1 over a range of price ratios r = P_new/P_old. Optionally overlays a 'breakeven' line for a given fee APY to show where fees compensate for IL.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "range": {
      "type": "string",
      "description": "JSON array [start, stop, n] generating n log-spaced ratios from start to stop. Default: [0.25, 4, 80] covers a 16x range each direction"
    },
    "fee_apy": {
      "type": "number",
      "description": "Fee APY (as decimal, e.g. 0.20 = 20%). If supplied, draws a breakeven horizontal at this level"
    },
    "verbose": {
      "type": "boolean",
      "description": "Include the IL derivation alongside the numeric curve. Default false"
    },
    "price_ratios": {
      "type": "string",
      "description": "JSON array of price ratios to evaluate (e.g. [0.25, 0.5, 1, 2, 4]). Alternative to 'range'"
    },
    "holding_period_days": {
      "type": "number",
      "description": "Holding period in days (default 365). Used with fee_apy to compute realized fee return"
    }
  }
}
    arguments 25 lines
  • petri_amm_quote unknown never probed

    Single-trade math for a Uniswap V2-style constant-product AMM. Given reserves and a trade size, returns the output amount, effective price, spot price, slippage, and fee paid. Pure algebra — no model required.

    mcp-tool

    {
  "type": "object",
  "required": [
    "reserve_x",
    "reserve_y",
    "amount_in"
  ],
  "properties": {
    "fee_bps": {
      "type": "number",
      "description": "Pool fee in basis points (default 30 = 0.3%, Uniswap V2 standard)"
    },
    "verbose": {
      "type": "boolean",
      "description": "Include the math derivation (formula + substitution) alongside the numeric result. Default false. Use when explaining to a user how the swap was priced"
    },
    "amount_in": {
      "type": "number",
      "description": "Amount of token X being swapped in"
    },
    "reserve_x": {
      "type": "number",
      "description": "Reserve of token X (input token)"
    },
    "reserve_y": {
      "type": "number",
      "description": "Reserve of token Y (output token)"
    }
  }
}
    arguments 30 lines
  • petri_analyze unknown never probed

    Analyze a Petri net model for behavioral properties including reachability, deadlocks, liveness, boundedness, and element importance.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "full": {
      "type": "boolean",
      "description": "Include sensitivity analysis (element importance, symmetry groups)"
    },
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON or tokenmodel DSL (S-expression format starting with '(')"
    }
  }
}
    arguments 16 lines
  • petri_app_get unknown never probed

    Look up a named app and return the spec it currently points at: its id, kind ('model', 'spec', or 'bundle'), and content.

    mcp-tool

    {
  "type": "object",
  "required": [
    "name"
  ],
  "properties": {
    "name": {
      "type": "string",
      "description": "App name, as given to petri_app_save."
    }
  }
}
    arguments 12 lines
  • petri_app_save unknown never probed

    Name a spec stored in the app store, so it can be found again with petri_app_get and rebuilt with petri_build(id=...). Recorded as name -> spec id; re-saving an existing name moves it to point at a new spec id.

    mcp-tool

    {
  "type": "object",
  "required": [
    "name",
    "id"
  ],
  "properties": {
    "id": {
      "type": "string",
      "description": "Spec id to name (must already exist in the app store)."
    },
    "name": {
      "type": "string",
      "description": "Human-given name for this app."
    }
  }
}
    arguments 17 lines
  • petri_build unknown never probed

    Generate a full, runnable Go application from a Petri net model, an Application spec, or a raw bundle document, write it to output_dir, and (by default) actually build and run it and verify it behaves correctly — not just that it compiles. Accepts exactly one of 'model' (single-net), 'spec' (+optional 'fusions', composed via one subnet per entity), 'bundle' (raw bundle document), or 'id' (a previously stored spec — wins over the others if given). Replaces petri_application, petri_bundle, and petri_codegen's language='go' option. Whenever the build resolves through a spec id (given directly via 'id', or freshly stored from 'model'/'spec'/'bundle'), a lineage edge recording this build's outcome is written to the app store — pass 'prompt' to also attach free-text intent. The result carries the resolved spec 'id'.

    mcp-tool

    {
  "type": "object",
  "required": [
    "output_dir"
  ],
  "properties": {
    "id": {
      "type": "string",
      "description": "Optional: build from a previously stored spec id instead of 'model'/'spec'/'bundle'. Wins over the others if given."
    },
    "spec": {
      "type": "string",
      "description": "Application specification as JSON (entities with fields/states/actions). Mutually exclusive with 'model', 'bundle' and 'id'."
    },
    "model": {
      "type": "string",
      "description": "Single-net Petri net model as JSON or tokenmodel DSL. Mutually exclusive with 'spec', 'bundle' and 'id'."
    },
    "bundle": {
      "type": "string",
      "description": "Raw bundle document JSON: {name, subnets: [{id, net_type, model}], links: [...]}. Mutually exclusive with 'model', 'spec' and 'id'."
    },
    "prompt": {
      "type": "string",
      "description": "Optional free-text description of intent, recorded alongside this build's lineage edge in the app store."
    },
    "verify": {
      "type": "boolean",
      "description": "Build and run the generated app and verify it against the model's own firing rule before returning (default: true). Set false to skip the Go toolchain / for fast iteration."
    },
    "fusions": {
      "type": "string",
      "description": "Optional JSON array of cross-entity rendezvous, used only with 'spec': [{\"id\":\"...\",\"members\":[{\"entity\":\"...\",\"action\":\"...\"}]}]"
    },
    "package": {
      "type": "string",
      "description": "Go package name, single-net models only (default: derived from model name)"
    },
    "extensions": {
      "type": "string",
      "description": "Optional JSON object with extensions for a single-net model: {\"roles\":[...], \"views\":[...], \"admin\":{...}, \"navigation\":{...}}"
    },
    "output_dir": {
      "type": "string",
      "description": "Directory to write the generated, standalone (own go.mod) application into. Required — this tool always writes to disk."
    },
    "module_path": {
      "type": "string",
      "description": "Go module/import path of the generated app (default: app/<name>)"
    }
  }
}
    arguments 52 lines
  • petri_canonical unknown never probed

    Exact automorphism orbits and an isomorphism-invariant canonical id for a model: two nets that are the same up to renaming get the same id, and places or transitions in one orbit are provably interchangeable positions in the net (individualization-refinement, McKay & Piperno 2014). Refuses past a 200,000-leaf search budget rather than guess. Use it to detect a template you already have, to dedupe a catalog, or to settle 'are these two pools the same knob' outright.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    }
  }
}
    arguments 12 lines
  • petri_code_to_flow unknown never probed

    Convert source code into a formal Petri net model. Analyzes code structure (control flow, state machines, resource management, concurrency) and produces an executable, verifiable Petri net — not just a diagram.

    mcp-tool

    {
  "type": "object",
  "required": [
    "code"
  ],
  "properties": {
    "code": {
      "type": "string",
      "description": "Source code to analyze and convert into a Petri net model"
    },
    "name": {
      "type": "string",
      "description": "Name for the generated model. Defaults to a name derived from the code."
    },
    "focus": {
      "type": "string",
      "description": "Analysis focus: control-flow (function call sequences), state-machine (state transitions), resources (resource allocation/consumption), concurrency (parallel processes, synchronization). Defaults to auto-detect."
    },
    "language": {
      "type": "string",
      "description": "Programming language of the source code (e.g., go, python, javascript, java). Auto-detected if omitted."
    }
  }
}
    arguments 24 lines
  • petri_codegen unknown never probed

    Generate executable code from a validated Petri net model. 'zk-go' produces gnark ZK circuits; 'go-core', 'rust', 'python', and 'javascript' produce a dependency-free single-file state-machine core (token places only, no expression guards) for embedding in an existing codebase; 'lean' produces the proof form — the generator model-checks the net and emits Lean 4 theorems the kernel re-derives at compile time. For a full event-sourced HTTP application (what 'language=go' used to do here), use petri_build instead — it writes a runnable app to disk and can verify it actually runs.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "form": {
      "type": "string",
      "description": "Core-mode implementation form: generated (default; arcs unrolled into straight-line code), interpreter (net as runtime data + generic engine), lambda (pure per-transition functions in a fixed schedule), contract (public entry points that refuse when not enabled). Ignored for 'go', 'zk-go', and 'lean' (always proof)."
    },
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON or tokenmodel DSL (S-expression format starting with '(')"
    },
    "package": {
      "type": "string",
      "description": "Package/module name for generated code"
    },
    "language": {
      "type": "string",
      "description": "Target language: zk-go (ZK circuits), go-core, rust, python, javascript (state-machine core), lean (proof form). 'go' is no longer accepted here — use petri_build."
    },
    "extensions": {
      "type": "string",
      "description": "Optional JSON object with extensions: {\"roles\":[...], \"views\":[...], \"admin\":{...}, \"navigation\":{...}}. These add authentication, views, and UI features to the generated code."
    }
  }
}
    arguments 28 lines
  • petri_conformance unknown never probed

    Check how well a Petri net model matches real observed behavior, by replaying an event log against it. Returns fitness (can the model reproduce the observed traces?), precision (does the model allow behavior never observed?), and per-trace diagnostics naming the activities that could not be replayed. Use after petri_validate/petri_verify to confirm the model describes reality, not just a self-consistent fiction. The log is replayed one case at a time from the model's initial marking, so the model should be the per-case workflow (one order, one patient); a resource net whose places are shared across cases will not fit.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "log"
  ],
  "properties": {
    "log": {
      "type": "string",
      "description": "Event log as a JSON array of events. Each event needs at least a case id and an activity:\n  [{\"case\":\"order-1\",\"activity\":\"receive\",\"timestamp\":\"2026-01-01T10:00:00Z\"},\n   {\"case\":\"order-1\",\"activity\":\"ship\"},\n   {\"case\":\"order-2\",\"activity\":\"receive\"}]\n\nAccepted key aliases: case/caseId/case_id/trace, activity/action/task/event/transition,\ntimestamp/time/ts (RFC3339; optional — events keep their given order when absent),\nresource (optional). Activity names must match the model's transition names."
    },
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON or tokenmodel DSL (S-expression format starting with '(')"
    },
    "include_traces": {
      "type": "boolean",
      "description": "Include per-trace fitness detail in the output (default true; set false for a large log)"
    }
  }
}
    arguments 21 lines
  • petri_corr_matrix unknown never probed

    Render a correlation matrix as a heatmap. Useful as a pre-flight check on petri_sde correlation inputs and as a general visualization of asset/observable relationships. Accepts pairwise rho format or a full matrix array.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "Optional title shown above the heatmap"
    },
    "labels": {
      "type": "string",
      "description": "JSON array of axis labels. Required when using 'matrix'. Optional when using 'correlations' (auto-extracted from keys)"
    },
    "matrix": {
      "type": "string",
      "description": "JSON nested array (full NxN matrix). Alternative to 'correlations'"
    },
    "correlations": {
      "type": "string",
      "description": "JSON object of pairwise correlations keyed by \"A-B\" with values in [-1, 1]. Place IDs/names are extracted from the keys. e.g. {\"btc-eth\": 0.85, \"btc-sol\": 0.7, \"eth-sol\": 0.75}. Alternative to 'matrix'"
    }
  }
}
    arguments 21 lines
  • petri_distribution unknown never probed

    Run N stochastic paths and visualize the distribution of an observable at the final time. Output: histogram + percentile band (P5 / P25 / P50 / P75 / P95) + mean and stdev. Answers questions like 'what's the probability the LP ends below $X' or 'what's the 5th-percentile worst case'. Mode selects SDE (continuous noise, for prices) or SSA (discrete events, for counts).

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "observable"
  ],
  "properties": {
    "bins": {
      "type": "number",
      "description": "Histogram bin count (default 30, max 100)"
    },
    "mode": {
      "type": "string",
      "description": "'sde' (continuous-noise GBM, requires volatility) or 'ssa' (discrete Gillespie events). Default 'sde'"
    },
    "seed": {
      "type": "number",
      "description": "Random seed for reproducibility (default 42)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "paths": {
      "type": "number",
      "description": "Number of paths (default 500, max 5000). Higher = tighter percentile estimates"
    },
    "rates": {
      "type": "string",
      "description": "JSON object of rate constants (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "tspan": {
      "type": "string",
      "description": "Integration span (default [0, 1])"
    },
    "observable": {
      "type": "string",
      "description": "Place ID whose final-time distribution is plotted"
    },
    "volatility": {
      "type": "string",
      "description": "Required for mode=sde. JSON object mapping place_id → sigma"
    }
  }
}
    arguments 45 lines
  • petri_docs unknown never probed

    Generate markdown documentation from a Petri net model with mermaid diagrams for visualization. Useful for exploring and understanding models.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON or tokenmodel DSL (S-expression format starting with '(')"
    },
    "include_metadata": {
      "type": "boolean",
      "description": "Include model metadata in documentation (default: true)"
    }
  }
}
    arguments 16 lines
  • petri_explain unknown never probed

    Explain the math behind any concept used in this MCP — formulas, intuition, derivations, worked examples, and what tool to try next. Without arguments, lists all available concepts. With a topic name, returns the full explanation.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "topic": {
      "type": "string",
      "description": "Concept name (e.g. 'impermanent_loss', 'constant_product_amm'). Omit to list all available topics with one-line summaries"
    }
  }
}
    arguments 9 lines
  • petri_extend unknown never probed

    Modify an existing Petri net model by applying operations. Operations: add_place, add_transition, add_arc, add_event, add_event_field, add_binding, remove_place, remove_transition, remove_arc, remove_event, remove_binding. Returns the modified model. Optionally persists to the content-addressed app store: pass 'id' to load the starting model from a previously stored spec instead of 'model' (if both are given, 'id' wins and 'model' is ignored), and/or 'prompt' to record the free-text intent behind this edit. Giving either triggers persistence — the starting spec (stored fresh as a root if 'id' was not given), the resulting spec, the prompt (if any), and a lineage edge are all recorded, and the result carries 'id' (the new spec's id) and 'parentId'. Calling with neither 'id' nor 'prompt' (the original shape) persists nothing and behaves exactly as before.

    mcp-tool

    {
  "type": "object",
  "required": [
    "operations"
  ],
  "properties": {
    "id": {
      "type": "string",
      "description": "Optional: load the starting model from this previously stored spec id instead of 'model'. Wins over 'model' if both are given. Triggers persistence of the result."
    },
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON. Required unless 'id' is given."
    },
    "prompt": {
      "type": "string",
      "description": "Optional: free-text description of the intent behind this edit, recorded in the app store's lineage. Triggers persistence of the result."
    },
    "operations": {
      "type": "string",
      "description": "JSON array of operations. Each operation has 'op' (operation type) and operation-specific fields. Examples: {\"op\":\"add_place\",\"id\":\"new_state\"}, {\"op\":\"add_transition\",\"id\":\"transfer\",\"event\":\"transferred\",\"guard\":\"balances[from] >= amount\",\"bindings\":[{\"name\":\"from\",\"type\":\"string\",\"keys\":[\"from\"]},{\"name\":\"amount\",\"type\":\"number\",\"value\":true}]}, {\"op\":\"add_arc\",\"from\":\"pending\",\"to\":\"approve\"}, {\"op\":\"add_event\",\"id\":\"transferred\",\"fields\":[{\"name\":\"from\",\"type\":\"string\"},{\"name\":\"amount\",\"type\":\"number\"}]}, {\"op\":\"add_binding\",\"transition\":\"transfer\",\"name\":\"to\",\"type\":\"string\",\"keys\":[\"to\"]}"
    }
  }
}
    arguments 24 lines
  • petri_fit unknown never probed

    Fit transition rates to observed data. Given (t, value) measurements for one or more places, finds rates that minimize squared error under mass-action ODE. Uses Nelder-Mead simplex. Returns fitted rates plus a plot of observations (dots) over the fitted trajectory.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "observations",
    "parameters"
  ],
  "properties": {
    "tol": {
      "type": "number",
      "description": "Convergence tolerance on simplex spread (default 1e-6)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "method": {
      "type": "string",
      "description": "Optimizer: \"nelder-mead\" (default, gradient-free) or \"adam\" (gradient-based on analytic forward sensitivities — typically fewer solves for smooth fits)"
    },
    "verbose": {
      "type": "boolean",
      "description": "Include the Nelder-Mead algorithm description in the response. Default false"
    },
    "max_iter": {
      "type": "number",
      "description": "Max optimizer iterations (default 200, max 1000)"
    },
    "parameters": {
      "type": "string",
      "description": "JSON object mapping transition_id → [min, max] bounds, e.g. {\"deliver\": [0.01, 10]}"
    },
    "fixed_rates": {
      "type": "string",
      "description": "JSON object of rates for transitions NOT being fit (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "observations": {
      "type": "string",
      "description": "JSON object: {place_id: [[t1, v1], [t2, v2], ...], ...}. Times are interpreted in model time units"
    },
    "initial_guess": {
      "type": "string",
      "description": "JSON object of starting rates for parameters being fit (default: midpoint of bounds)"
    }
  }
}
    arguments 46 lines
  • petri_frontend unknown never probed

    Generate a vanilla JavaScript ES modules frontend application from a Petri net model. Produces a Vite + ES modules project with API client, state display, and transition forms using plain JavaScript.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON or tokenmodel DSL (S-expression format starting with '(')"
    },
    "api_url": {
      "type": "string",
      "description": "Backend API base URL (default: http://localhost:8080)"
    },
    "project": {
      "type": "string",
      "description": "Project name for package.json (default: model name)"
    },
    "extensions": {
      "type": "string",
      "description": "Optional JSON object with extensions: {\"roles\":[...], \"views\":[...], \"admin\":{...}, \"navigation\":{...}}. These add authentication, views, and UI features to the generated frontend."
    }
  }
}
    arguments 24 lines
  • petri_history unknown never probed

    Return the full lineage of a spec stored in the app store: the chain of prompts, edits and builds that produced it, root first. Each entry names the tool activity ('petri_extend', 'petri_build', ...) that touched that spec, the prompt (if any) that motivated it, and a short outcome note.

    mcp-tool

    {
  "type": "object",
  "required": [
    "id"
  ],
  "properties": {
    "id": {
      "type": "string",
      "description": "Spec id to show history for (as returned by petri_extend or petri_build)."
    }
  }
}
    arguments 12 lines
  • petri_invariants unknown never probed

    Derive a model's full algebraic invariant structure: conservation laws (Farkas P-invariants — weighted place sums every run preserves), firing cycles (T-invariants, named per cycle, each tagged StructuralProof), and the siphon/trap report (every minimal siphon and trap, plus deadlock witnesses — minimal siphons empty at the initial marking, which proves every transition needing one permanently disabled). Pure structure, no simulation; every claim holds for every trajectory from this initial marking. Caveats name what the analysable net encoded lossily or losslessly (capacity as a post-firing bound, a read arc as a reversed inhibitor, a dropped guard). Strictly more than the P-invariants petri_validate and petri_analyze report.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    }
  }
}
    arguments 12 lines
  • petri_migrate unknown never probed

    Migrate a Petri net model from v1 (flat) to v2 (nested) schema format. V2 format separates the net definition from extensions like roles and views.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "The v1 Petri net model as a JSON string"
    }
  }
}
    arguments 12 lines
  • petri_ode unknown never probed

    Run an ODE (mass-action kinetics) simulation of the Petri net using the Tsit5 solver (matches pflow.xyz). Returns a downsampled time series of place concentrations and an inline PNG plot. Use mode=equilibrium to integrate until the system stabilizes. Refuses (Diverged=true) a model with a read arc, inhibitor, reached capacity or guard rather than silently ignore it — use petri_stochastic for those. See the simulation_choice topic (petri_explain) for the full decision rule, including why weight>1 arcs get a genuinely different rate law here than in petri_stochastic.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "mode": {
      "type": "string",
      "description": "'solve' (full trajectory, default) or 'equilibrium' (stop at steady state)"
    },
    "plot": {
      "type": "boolean",
      "description": "Include inline PNG plot (default true). Ignored when layout is set."
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "rates": {
      "type": "string",
      "description": "Optional JSON object mapping transition_id to rate (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "tspan": {
      "type": "string",
      "description": "Optional JSON array [t0, tf] (default [0, 10])"
    },
    "layout": {
      "type": "string",
      "description": "Output layout: 'plot' (default, trajectory only), 'combined' (net snapshot at final marking + plot side-by-side), or 'net' (net snapshot only, no plot)"
    },
    "method": {
      "type": "string",
      "description": "'tsit5' (default), 'rk45', 'rk4', or 'euler'"
    },
    "samples": {
      "type": "number",
      "description": "Max trajectory samples returned (default 200, downsampled if needed)"
    },
    "verbose": {
      "type": "boolean",
      "description": "Include the algorithm description and formula in the response. Default false. Use to teach a user what the solver actually computed"
    },
    "variables": {
      "type": "string",
      "description": "Optional JSON array of place IDs to plot (default: all places)"
    }
  }
}
    arguments 48 lines
  • petri_ode_sensitivity unknown never probed

    ODE sensitivity analysis: perturb each transition's rate by a small delta and measure how much an observable's equilibrium value moves. Returns dimensionless elasticities per transition plus an inline net diagram tinted by influence. Use when you want to know which knobs matter for dynamics, not just structure.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "observable"
  ],
  "properties": {
    "delta": {
      "type": "number",
      "description": "Perturbation fraction (default 0.05 = 5%)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "title": {
      "type": "string",
      "description": "Title shown above the sensitivity diagram"
    },
    "tspan": {
      "type": "string",
      "description": "Per-run integration span (default [0, 50])"
    },
    "method": {
      "type": "string",
      "description": "\"fd\" (default): finite-difference perturbation runs, one per transition. \"analytic\": exact forward sensitivities from ONE augmented solve (go-pflow learn) — faster for many transitions and derivative-exact; measures d(observable at t_end)/d(rate) rather than a +delta%% re-run"
    },
    "base_rates": {
      "type": "string",
      "description": "JSON object of base rates per transition (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "observable": {
      "type": "string",
      "description": "Place ID whose equilibrium value is the target metric"
    }
  }
}
    arguments 37 lines
  • petri_optimize unknown never probed

    Multi-objective optimization over transition rates. Monte Carlo samples the parameter space, runs each combo to equilibrium, identifies the Pareto frontier (non-dominated points), and visualizes it. Returns JSON of every sample (with is_pareto flag) plus a scatter plot (2 objectives) or parallel-coordinates chart (3+).

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "parameters",
    "objectives"
  ],
  "properties": {
    "seed": {
      "type": "number",
      "description": "Random seed for reproducibility (default 42)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "tspan": {
      "type": "string",
      "description": "Per-run integration span (default [0, 50])"
    },
    "samples": {
      "type": "number",
      "description": "Number of Monte Carlo samples (default 200, max 2000)"
    },
    "verbose": {
      "type": "boolean",
      "description": "Include the Monte Carlo / Pareto algorithm description in the response. Default false"
    },
    "objectives": {
      "type": "string",
      "description": "JSON array of objectives. Each entry: {\"place\": \"place_id\", \"direction\": \"max\"|\"min\"}. e.g. [{\"place\":\"delivered\",\"direction\":\"max\"}, {\"place\":\"refunded\",\"direction\":\"min\"}]"
    },
    "parameters": {
      "type": "string",
      "description": "JSON object mapping transition_id → [min, max] rate range. e.g. {\"start_brew\": [0.1, 5.0], \"deliver\": [0.5, 2.0]}"
    },
    "fixed_rates": {
      "type": "string",
      "description": "JSON object of transition rates held constant during the sweep (default: the rate each transition declares in the model, 1.0 where none)"
    }
  }
}
    arguments 42 lines
  • petri_param_heatmap unknown never probed

    2D parameter sweep: vary two rate constants over a grid, run each combo to equilibrium, render the observable as a viridis heatmap. Answers 'how does APY depend on fee_tier × liquidity?' or 'which parameter regime gives me the equilibrium I want?'.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "param_x",
    "param_y",
    "observable",
    "range_x",
    "range_y"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "tspan": {
      "type": "string",
      "description": "Per-run integration span (default [0, 50])"
    },
    "param_x": {
      "type": "string",
      "description": "First transition ID to sweep"
    },
    "param_y": {
      "type": "string",
      "description": "Second transition ID to sweep"
    },
    "range_x": {
      "type": "string",
      "description": "JSON array [start, stop, n] for x sweep (e.g. [0.1, 5.0, 20])"
    },
    "range_y": {
      "type": "string",
      "description": "JSON array [start, stop, n] for y sweep"
    },
    "log_scale": {
      "type": "boolean",
      "description": "Sweep parameters in log space (better for ranges spanning orders of magnitude). Default false"
    },
    "observable": {
      "type": "string",
      "description": "Place ID whose equilibrium value is the heatmap color"
    },
    "fixed_rates": {
      "type": "string",
      "description": "JSON object of other transition rates held constant"
    }
  }
}
    arguments 49 lines
  • petri_phase_plot unknown never probed

    Phase-space portrait: run an ODE, project trajectory into the (place_x, place_y) plane (no time axis). Reveals attractors, limit cycles, and geometric structure of two-state dynamics. Optionally overlays multiple trajectories from different initial conditions to map the basin structure.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "place_x",
    "place_y"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "rates": {
      "type": "string",
      "description": "JSON object of rate constants (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "title": {
      "type": "string",
      "description": "Optional title shown above the plot"
    },
    "tspan": {
      "type": "string",
      "description": "Integration span (default [0, 20])"
    },
    "place_x": {
      "type": "string",
      "description": "Place ID for the x-axis"
    },
    "place_y": {
      "type": "string",
      "description": "Place ID for the y-axis"
    },
    "initial_conditions": {
      "type": "string",
      "description": "Optional JSON array of starting marking overrides, one per trajectory to draw. e.g. [{\"place_x\": 0.5}, {\"place_x\": 1.5}, {\"place_x\": 2.5}]. Default: a single trajectory from the model's initial marking"
    }
  }
}
    arguments 38 lines
  • petri_preview unknown never probed

    Preview a single generated file without full code generation. Use this to check specific files before committing to full generation. Available templates: main, workflow, events, aggregate, api, openapi, test, config, migrations, auth, middleware, permissions, views, navigation, admin, debug

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "file"
  ],
  "properties": {
    "file": {
      "type": "string",
      "description": "Template name to preview (e.g., 'api', 'workflow', 'events', 'aggregate', 'main')"
    },
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON"
    }
  }
}
    arguments 17 lines
  • petri_rate_scan unknown never probed

    Parameter sweep: vary one transition's mass-action rate over a list of values, run each to equilibrium, plot observables (steady-state place concentrations) vs the swept rate. Returns JSON of all results plus an inline PNG.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "transition"
  ],
  "properties": {
    "plot": {
      "type": "boolean",
      "description": "Include inline PNG plot (default true)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "range": {
      "type": "string",
      "description": "JSON array [start, stop, n] generating n equally-spaced rate values from start to stop. Alternative to 'values'"
    },
    "tspan": {
      "type": "string",
      "description": "Per-run integration span (default [0, 50]). Must be long enough for the system to settle at each rate"
    },
    "values": {
      "type": "string",
      "description": "JSON array of rate values to test (e.g. [0.1, 0.5, 1.0, 2.0, 5.0]). Either this or 'range' is required"
    },
    "transition": {
      "type": "string",
      "description": "Transition ID whose rate is being swept"
    },
    "fixed_rates": {
      "type": "string",
      "description": "JSON object of other transition rates held constant during the sweep (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "observables": {
      "type": "string",
      "description": "JSON array of place IDs to track at equilibrium (default: all places)"
    }
  }
}
    arguments 41 lines
  • petri_risk unknown never probed

    Risk dashboard for an observable under SDE simulation. Runs N paths, computes mean / stdev / P5 / P50 / P95 of final values, plus max drawdown and CVaR (expected shortfall in the worst 5% of paths). Output: composite card with stats panel and drawdown distribution histogram. Answers 'what's the worst-case loss?' and 'how bad does it usually get?'.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "observable",
    "volatility"
  ],
  "properties": {
    "seed": {
      "type": "number",
      "description": "Random seed (default 42)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "paths": {
      "type": "number",
      "description": "Number of SDE paths (default 500, max 5000)"
    },
    "rates": {
      "type": "string",
      "description": "JSON object of rate constants (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "steps": {
      "type": "number",
      "description": "Euler-Maruyama step count per path (default 200)"
    },
    "tspan": {
      "type": "string",
      "description": "Integration span (default [0, 1])"
    },
    "observable": {
      "type": "string",
      "description": "Place ID to monitor as the asset/portfolio value"
    },
    "volatility": {
      "type": "string",
      "description": "JSON object {place_id: sigma} for SDE noise"
    },
    "correlations": {
      "type": "string",
      "description": "Optional pairwise correlation dict (same format as petri_sde)"
    }
  }
}
    arguments 46 lines
  • petri_sankey unknown never probed

    Sankey-style flow diagram: run an ODE, compute integrated flow through each arc, render the net with arc widths proportional to flow magnitude. Reads as 'where is the money going' rather than 'which transitions fire'. Use to communicate token flow to non-Petri-net audiences.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "rates": {
      "type": "string",
      "description": "JSON object of rate constants (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "title": {
      "type": "string",
      "description": "Optional title shown above the diagram"
    },
    "tspan": {
      "type": "string",
      "description": "Integration span (default [0, 10])"
    }
  }
}
    arguments 24 lines
  • petri_scenario unknown never probed

    Answer a what-if about a Petri net: override the initial marking (staff on duty, stock on hand, queue depth), override rates, or vary a rate over time, then run it forward. Returns a trajectory plus operator metrics — throughput, mean and P95 per place, resource utilization, time-to-depletion. Use `scenarios` to compare several on one seed, which is the only way to tell a real difference from the dice. Pure: it never changes the model.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "seed": {
      "type": "number",
      "description": "Random seed (default 1, so an unconfigured call is still repeatable)"
    },
    "hours": {
      "type": "number",
      "description": "How far forward to run, in the model's time unit (default 1)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON, bundle document, or tokenmodel DSL"
    },
    "rates": {
      "type": "string",
      "description": "JSON object overriding transition rates for the whole horizon — {\"order_latte\": 30}"
    },
    "engine": {
      "type": "string",
      "description": "'ssa' (default, discrete) or 'ode' (continuous). SSA is right for staffing and queueing, where counts are small enough that noise decides the outcome. The ODE refuses models whose constraints it cannot represent rather than answering a less constrained question"
    },
    "marking": {
      "type": "string",
      "description": "JSON object overriding the initial marking, place by place — {\"staff/available\": 3}. Sparse: places you do not name keep what the model declares. Unknown place names are an error, not a silent no-op"
    },
    "samples": {
      "type": "number",
      "description": "Time points to report (default 60)"
    },
    "schedule": {
      "type": "string",
      "description": "JSON object making a rate vary over time — {\"order_latte\": [{\"until\": 2, \"value\": 40}, {\"until\": 8, \"value\": 12}]}. This is how you express a morning rush; a constant rate cannot, and averaging one away hides whether the queue recovers"
    },
    "scenarios": {
      "type": "string",
      "description": "JSON array of named scenarios to compare, each with its own marking/rates/schedule — [{\"name\":\"today\",\"marking\":{\"staff/available\":2}},{\"name\":\"one more\",\"marking\":{\"staff/available\":3}}]. All run on one seed. When set, the top-level marking/rates/schedule are ignored"
    },
    "realizations": {
      "type": "number",
      "description": "Independent stochastic runs to average (default 1). A single run of a queue is an anecdote"
    }
  }
}
    arguments 48 lines
  • petri_sde unknown never probed

    Stochastic Differential Equation simulation. Mass-action drift (as in petri_ode) plus EXOGENOUS geometric Brownian motion on user-selected places — for DeFi price processes, interest rate models, anywhere continuous noise scales with state value. This is not the net's own firing noise (that's petri_stochastic's job); it layers external uncertainty on top of the ODE. Returns mean ± stdev band over N paths.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "volatility"
  ],
  "properties": {
    "seed": {
      "type": "number",
      "description": "Random seed for reproducibility (default 42)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "paths": {
      "type": "number",
      "description": "Number of independent SDE paths (default 20, max 100). Mean and ±stdev are computed across paths"
    },
    "rates": {
      "type": "string",
      "description": "JSON object of mass-action rate constants (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "steps": {
      "type": "number",
      "description": "Euler-Maruyama step count (default 500). Higher = more accurate noise integration"
    },
    "tspan": {
      "type": "string",
      "description": "Integration span (default [0, 1])"
    },
    "samples": {
      "type": "number",
      "description": "Output sample count (default 200, downsampled from steps)"
    },
    "verbose": {
      "type": "boolean",
      "description": "Include the Euler-Maruyama algorithm description in the response. Default false"
    },
    "variables": {
      "type": "string",
      "description": "JSON array of place IDs to plot (default: all places)"
    },
    "volatility": {
      "type": "string",
      "description": "JSON object mapping place_id → sigma (annualized vol). Places not in this map evolve deterministically. e.g. {\"price_token_a\": 0.6, \"price_token_b\": 0.4}"
    },
    "correlations": {
      "type": "string",
      "description": "Optional JSON object of pairwise correlation coefficients in [-1, 1], keyed by \"placeA-placeB\" (sorted alphabetically). e.g. {\"btc-eth\": 0.7, \"btc-sol\": 0.6, \"eth-sol\": 0.5}. Missing pairs default to 0 (independent). Matrix must be positive semi-definite or the call errors."
    }
  }
}
    arguments 53 lines
  • petri_simulate unknown never probed

    Simulate firing transitions and see state changes. Returns detailed step-by-step state trace. Use this to verify workflow behavior before code generation.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON"
    },
    "steps": {
      "type": "string",
      "description": "JSON array of simulation steps with optional bindings: [{\"transition\":\"id\",\"bindings\":{...}}]. For simple cases, you can also use 'transitions' parameter."
    },
    "transitions": {
      "type": "string",
      "description": "JSON array of transition IDs to fire in order (simple alternative to 'steps')"
    }
  }
}
    arguments 20 lines
  • petri_stochastic unknown never probed

    Gillespie Stochastic Simulation Algorithm (SSA) over the Petri net's discrete marking. Distinct from petri_ode's continuous ODE — token counts stay integer, firings are random events, results have visible noise. Use when token counts are small enough that variance matters, or when the model has a read arc, inhibitor, reached capacity or guard that petri_ode refuses to model. Multiple realizations show mean ± stdev band. See the simulation_choice topic (petri_explain) for the full decision rule.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "seed": {
      "type": "number",
      "description": "Random seed for reproducibility (default 42)"
    },
    "model": {
      "type": "string",
      "description": "Petri net model JSON or tokenmodel DSL"
    },
    "rates": {
      "type": "string",
      "description": "JSON object of mass-action rate constants per transition (default: the rate each transition declares in the model, 1.0 where none)"
    },
    "tspan": {
      "type": "string",
      "description": "Integration span [t0, tf] (default [0, 10])"
    },
    "samples": {
      "type": "number",
      "description": "Number of time points to record per realization (default 200)"
    },
    "verbose": {
      "type": "boolean",
      "description": "Include the Gillespie SSA algorithm description in the response. Default false"
    },
    "variables": {
      "type": "string",
      "description": "JSON array of place IDs to plot (default: all places)"
    },
    "realizations": {
      "type": "number",
      "description": "Number of independent SSA runs (default 1, max 50). With >1, mean and ±stdev band are plotted"
    },
    "record_events": {
      "type": "boolean",
      "description": "Also return every firing of every realization as `paths` — the input shape petri_fit_discrete takes, so a simulated run can be fitted back (or a real event log can be checked against one). Off by default: a busy net over a long horizon is thousands of events"
    }
  }
}
    arguments 44 lines
  • petri_template unknown never probed

    DeFi/tokenomics model templates ready for analysis. Without a name argument, lists all available templates with one-line descriptions. With a name, returns the full model JSON ready to feed into petri_visualize / petri_ode / petri_optimize / etc.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Template name (e.g. 'constant_product_amm'). Omit to list all templates with descriptions"
    }
  }
}
    arguments 9 lines
  • petri_validate unknown never probed

    Validate a Petri net model for structural correctness. Checks for empty models, unconnected elements, and invalid arc references.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON or tokenmodel DSL (S-expression format starting with '(')"
    }
  }
}
    arguments 12 lines
  • petri_verify unknown never probed

    Check whether a Petri net model satisfies stated correctness properties. Returns proved/refuted/unknown per property. Refutations include a firing sequence you can replay with petri_simulate to reproduce the failure. Use this to answer 'is this model correct?' against explicit requirements, rather than eyeballing a simulation.

    mcp-tool

    {
  "type": "object",
  "required": [
    "model",
    "properties"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON or tokenmodel DSL (S-expression format starting with '(')"
    },
    "max_states": {
      "type": "string",
      "description": "State exploration limit for exhaustive checks (default 20000). Structural proofs ignore this."
    },
    "properties": {
      "type": "string",
      "description": "JSON array of properties to check. Each entry is either a shorthand string or an object.\n\nShorthand strings:\n  \"deadlock-free\"            no reachable marking is a deadlock\n  \"bounded\"                  no place accumulates tokens without limit\n  \"live\"                     every transition can fire from some reachable marking\n  \"terminating\"              every execution eventually stops\n  \"conserves\"                total token count never changes\n  \"reachable:done=1\"         some reachable marking matches (partial markings allowed)\n  \"unreachable:a=1,b=1\"      no reachable marking matches — the safety form\n  \"mutex:busy1,busy2\"        at most one of these places holds a token (\"mutex:a,b,c<=2\" for a higher bound)\n  \"a + 2*b == 10\"            a linear relation over places holding at every reachable marking\n\nObject form (equivalent, more explicit):\n  {\"kind\":\"invariant\",\"name\":\"supply is conserved\",\"expr\":\"minted == circulating + burned\"}\n  {\"kind\":\"unreachable\",\"name\":\"no double spend\",\"target\":{\"spent\":2}}\n  {\"kind\":\"mutual-exclusion\",\"places\":[\"busy1\",\"busy2\"],\"bound\":1}\n\nExample: [\"deadlock-free\",\"bounded\",\"mutex:busy1,busy2\",\"minted == circulating + burned\"]"
    }
  }
}
    arguments 21 lines
  • petri_visualize unknown never probed

    Generate an SVG visualization of a Petri net model showing places, transitions, and arcs. Returns both SVG text and an inline PNG. Supports shading: 'sensitivity' (run analyzer and tint by element importance) or 'marking' (color places by user-supplied values).

    mcp-tool

    {
  "type": "object",
  "required": [
    "model"
  ],
  "properties": {
    "model": {
      "type": "string",
      "description": "The Petri net model as JSON or tokenmodel DSL (S-expression format starting with '(')"
    },
    "shade": {
      "type": "string",
      "description": "'none' (default), 'sensitivity' (color by analyzer importance), or 'marking' (color places by marking values)"
    },
    "title": {
      "type": "string",
      "description": "Optional title shown above the diagram"
    },
    "marking": {
      "type": "string",
      "description": "Optional JSON object {place_id: value} overriding the initial marking. Used as both label values and (with shade=marking) fill saturation"
    }
  }
}
    arguments 24 lines
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