_ registry / mcp http-sse · checked 1h ago

AgentStack

https://agentstack-mcp.pages.dev

Registry code: e037c795a25e0d8a

api record

Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.

from a public catalogue that lists it, not from the operator

endpoint
https://agentstack-mcp.pages.dev/mcp
protocol
http-sse ·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
70ms

last good check

priced tools
0

of 24 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 24 tools
3 open 21 never probed 3 of 24 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.

  • calc_list_metrics open 1h ago

    COMPUTE. List every supported metric with descriptions and required/optional params. No parameters.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • health_check open 1h ago

    Aggregated health/status for the whole stack (all three engines + composites). No parameters.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • decide_list_methods open 1h ago

    DECIDE. List the scoring methods (weighted_sum, weighted_product, topsis) with normalization details and when to use each. No parameters.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • sim_run unknown never probed

    SIMULATE. Deterministic what-if projection from a template (saas_growth, pricing_change, churn_impact, cost_reduction, hiring_plan, cash_runway, unit_economics, marketing_funnel, compound_growth) or a free-form 'metrics' model. Returns per-period projections, key_results, assumptions_used, methodology, and an explanation.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "inputs": {
      "type": "object"
    },
    "horizon": {
      "type": "integer"
    },
    "metrics": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "template": {
      "type": "string"
    },
    "period_label": {
      "type": "string"
    }
  }
}
    arguments 23 lines
  • sim_break_even unknown never probed

    SIMULATE. Solve for the scenario input value required to make an output metric hit a target value (deterministic bisection). Requires 'template', 'solve_for', 'target_value'.

    mcp-tool

    {
  "type": "object",
  "required": [
    "template",
    "solve_for",
    "target_value"
  ],
  "properties": {
    "bounds": {
      "type": "array",
      "items": {
        "type": "number"
      }
    },
    "inputs": {
      "type": "object"
    },
    "horizon": {
      "type": "integer"
    },
    "template": {
      "type": "string"
    },
    "solve_for": {
      "type": "string"
    },
    "period_label": {
      "type": "string"
    },
    "target_value": {
      "type": "number"
    },
    "target_metric": {
      "type": "string"
    }
  }
}
    arguments 37 lines
  • calc_metric unknown never probed

    COMPUTE. Exact business/SaaS/finance metric: ltv, cac, ltv_cac_ratio, payback_period_months, contribution_margin, gross_margin, churn_rate, mrr_growth_rate, arr, break_even_units, nrr, grr, rule_of_40, magic_number. Rates/margins are decimals (0.05=5%). Call calc_list_metrics for schemas.

    mcp-tool

    {
  "type": "object",
  "required": [
    "metric",
    "params"
  ],
  "properties": {
    "metric": {
      "type": "string"
    },
    "params": {
      "type": "object"
    },
    "currency": {
      "type": "string"
    }
  }
}
    arguments 18 lines
  • sim_compare unknown never probed

    SIMULATE. Run 2-3 scenarios and compare their key_results side by side with deltas vs the first (baseline). Optional 'compare_metric' + 'goal' (max|min) picks a winner.

    mcp-tool

    {
  "type": "object",
  "required": [
    "scenarios"
  ],
  "properties": {
    "goal": {
      "type": "string"
    },
    "horizon": {
      "type": "integer"
    },
    "scenarios": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "compare_metric": {
      "type": "string"
    },
    "include_projections": {
      "type": "boolean"
    }
  }
}
    arguments 26 lines
  • sim_list_templates unknown never probed

    SIMULATE. List every scenario template (inputs, defaults, outputs) plus the custom-model format and period labels. No parameters.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • decide_compare_two unknown never probed

    DECIDE. Head-to-head comparison of exactly two options with per-criterion win counts and margin. Pass option_a/option_b (or a 2-element options array), criteria, and scores.

    mcp-tool

    {
  "type": "object",
  "required": [
    "criteria",
    "scores"
  ],
  "properties": {
    "method": {
      "type": "string"
    },
    "scores": {
      "type": "object"
    },
    "options": {
      "type": "array"
    },
    "criteria": {
      "type": "array"
    },
    "option_a": {
      "type": "string"
    },
    "option_b": {
      "type": "string"
    }
  }
}
    arguments 27 lines
  • calc_currency_convert unknown never probed

    COMPUTE. Convert between major currencies (USD, EUR, GBP, JPY, CAD, AUD, CHF, CNY, INR) with Decimal precision. Static offline table by default; live/historical ECB rates via date/live=true.

    mcp-tool

    {
  "type": "object",
  "required": [
    "amount",
    "from_currency",
    "to_currency"
  ],
  "properties": {
    "date": {
      "type": "string"
    },
    "live": {
      "type": "boolean"
    },
    "amount": {
      "type": "number"
    },
    "to_currency": {
      "type": "string"
    },
    "from_currency": {
      "type": "string"
    }
  }
}
    arguments 25 lines
  • calc_business_days unknown never probed

    COMPUTE. Business-day arithmetic honoring weekends + regional holidays. operation: add_business_days | count_business_days | next_business_day | previous_business_day. region: US | UK | EU | NONE.

    mcp-tool

    {
  "type": "object",
  "required": [
    "operation",
    "start_date"
  ],
  "properties": {
    "days": {
      "type": "integer"
    },
    "region": {
      "type": "string"
    },
    "end_date": {
      "type": "string"
    },
    "operation": {
      "type": "string"
    },
    "start_date": {
      "type": "string"
    },
    "custom_holidays": {
      "type": "array",
      "items": {
        "type": "string"
      }
    }
  }
}
    arguments 30 lines
  • list_capabilities unknown never probed

    Discovery: the three namespaces (sim_*, decide_*, calc_*), the cross-domain composite tools, the available ?profile= filters, and links to the standalone servers. Call this first to see everything AgentStack exposes. No parameters.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • sim_sensitivity unknown never probed

    SIMULATE. Vary one or more scenario inputs and show the impact on a target output metric (one-at-a-time), with elasticity + most-influential ranking. Requires 'template' and 'variable' (or 'variables').

    mcp-tool

    {
  "type": "object",
  "required": [
    "template"
  ],
  "properties": {
    "max": {
      "type": "number"
    },
    "min": {
      "type": "number"
    },
    "steps": {
      "type": "integer"
    },
    "inputs": {
      "type": "object"
    },
    "values": {
      "type": "array",
      "items": {
        "type": "number"
      }
    },
    "horizon": {
      "type": "integer"
    },
    "template": {
      "type": "string"
    },
    "variable": {
      "type": "string"
    },
    "variables": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "variation": {
      "type": "number"
    },
    "period_label": {
      "type": "string"
    },
    "target_metric": {
      "type": "string"
    }
  }
}
    arguments 50 lines
  • calc_irr unknown never probed

    COMPUTE. Internal Rate of Return: per-period rate where NPV=0 (Newton + bisection). Requires a sign change in cashflows.

    mcp-tool

    {
  "type": "object",
  "required": [
    "cashflows"
  ],
  "properties": {
    "guess": {
      "type": "number"
    },
    "cashflows": {
      "type": "array",
      "items": {
        "type": "number"
      }
    }
  }
}
    arguments 17 lines
  • calc_loan_amortization unknown never probed

    COMPUTE. Level-payment loan: monthly payment, total interest, payoff, and (optional) full schedule.

    mcp-tool

    {
  "type": "object",
  "required": [
    "principal",
    "annual_rate",
    "term_months"
  ],
  "properties": {
    "currency": {
      "type": "string"
    },
    "principal": {
      "type": "number"
    },
    "annual_rate": {
      "type": "number"
    },
    "term_months": {
      "type": "integer"
    },
    "extra_payment": {
      "type": "number"
    },
    "include_schedule": {
      "type": "boolean"
    }
  }
}
    arguments 28 lines
  • calc_depreciation unknown never probed

    COMPUTE. Asset depreciation schedule. method: straight_line | declining_balance | sum_of_years_digits.

    mcp-tool

    {
  "type": "object",
  "required": [
    "method",
    "cost",
    "salvage_value",
    "useful_life_years"
  ],
  "properties": {
    "cost": {
      "type": "number"
    },
    "method": {
      "type": "string"
    },
    "currency": {
      "type": "string"
    },
    "salvage_value": {
      "type": "number"
    },
    "useful_life_years": {
      "type": "integer"
    }
  }
}
    arguments 26 lines
  • decide unknown never probed

    DECIDE. Rank named options against weighted criteria and return the winner, full ranking, per-criterion breakdowns, methodology, weights, and an explanation. Provide options, criteria [{name, weight, direction}], and a scores matrix. method: weighted_sum (default) | weighted_product | topsis.

    mcp-tool

    {
  "type": "object",
  "required": [
    "options",
    "criteria",
    "scores"
  ],
  "properties": {
    "method": {
      "type": "string"
    },
    "scores": {
      "type": "object"
    },
    "options": {
      "type": "array"
    },
    "criteria": {
      "type": "array"
    }
  }
}
    arguments 22 lines
  • decide_score unknown never probed

    DECIDE. Return the full normalized scored matrix (per-option, per-criterion) + ranking when scores are supplied separately, without the winner narrative.

    mcp-tool

    {
  "type": "object",
  "required": [
    "options",
    "criteria",
    "scores"
  ],
  "properties": {
    "method": {
      "type": "string"
    },
    "scores": {
      "type": "object"
    },
    "options": {
      "type": "array"
    },
    "criteria": {
      "type": "array"
    }
  }
}
    arguments 22 lines
  • decide_sensitivity unknown never probed

    DECIDE. Test how robust the decision winner is to changes in CRITERIA WEIGHTS (distinct from sim_sensitivity, which varies scenario inputs). Sweeps each weight +/-variation and reports a robustness score + flip points.

    mcp-tool

    {
  "type": "object",
  "required": [
    "options",
    "criteria",
    "scores"
  ],
  "properties": {
    "steps": {
      "type": "integer"
    },
    "method": {
      "type": "string"
    },
    "scores": {
      "type": "object"
    },
    "options": {
      "type": "array"
    },
    "criteria": {
      "type": "array"
    },
    "variation": {
      "type": "number"
    }
  }
}
    arguments 28 lines
  • calc_compound_growth unknown never probed

    COMPUTE. Compound-interest/growth math. operation: future_value | present_value | cagr. rate is annual decimal; compounding: daily|weekly|monthly|quarterly|semiannually|annually|continuous.

    mcp-tool

    {
  "type": "object",
  "required": [
    "operation"
  ],
  "properties": {
    "rate": {
      "type": "number"
    },
    "years": {
      "type": "number"
    },
    "currency": {
      "type": "string"
    },
    "end_value": {
      "type": "number"
    },
    "operation": {
      "type": "string"
    },
    "begin_value": {
      "type": "number"
    },
    "compounding": {
      "type": "string"
    },
    "future_value": {
      "type": "number"
    },
    "present_value": {
      "type": "number"
    }
  }
}
    arguments 35 lines
  • calc_npv unknown never probed

    COMPUTE. Net Present Value (discounted cash flow). NPV = sum(CF_t/(1+rate)^t); cashflows[0] is period 0 (usually the negative outlay).

    mcp-tool

    {
  "type": "object",
  "required": [
    "rate",
    "cashflows"
  ],
  "properties": {
    "rate": {
      "type": "number"
    },
    "currency": {
      "type": "string"
    },
    "cashflows": {
      "type": "array",
      "items": {
        "type": "number"
      }
    }
  }
}
    arguments 21 lines
  • plan_to_valuation unknown never probed

    COMPOSITE (simulate -> compute). Project a scenario, take a per-period cash-flow line from its projections ('cashflow_metric', e.g. 'mrr' or 'net_burn'), and value it exactly: NPV at a discount 'rate', IRR, and undiscounted total. Optional 'initial_investment' becomes the period-0 outflow (needed for IRR). Combines ScenarioSim + PrecisionCalc.

    mcp-tool

    {
  "type": "object",
  "required": [
    "template",
    "cashflow_metric",
    "rate"
  ],
  "properties": {
    "rate": {
      "type": "number"
    },
    "inputs": {
      "type": "object"
    },
    "horizon": {
      "type": "integer"
    },
    "metrics": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "currency": {
      "type": "string"
    },
    "template": {
      "type": "string"
    },
    "period_label": {
      "type": "string"
    },
    "cashflow_metric": {
      "type": "string"
    },
    "initial_investment": {
      "type": "number"
    }
  }
}
    arguments 40 lines
  • evaluate_options_with_scenarios unknown never probed

    COMPOSITE (simulate -> decide). Project each option as its own scenario, then rank the options against weighted criteria drawn from the scenario OUTCOMES. Provide a base 'template', an 'options' array ([{name, inputs}]), and 'criteria' ([{metric, weight, direction}]) where each metric is a scenario key_result. Combines ScenarioSim + DecisionMatrix.

    mcp-tool

    {
  "type": "object",
  "required": [
    "options",
    "criteria"
  ],
  "properties": {
    "inputs": {
      "type": "object"
    },
    "method": {
      "type": "string"
    },
    "horizon": {
      "type": "integer"
    },
    "options": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "criteria": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "template": {
      "type": "string"
    },
    "period_label": {
      "type": "string"
    }
  }
}
    arguments 36 lines
  • stress_test_decision unknown never probed

    COMPOSITE (simulate x decide). Take an options-vs-scenarios decision and stress ONE scenario assumption across a range applied to every option; report how often the baseline winner survives (robustness) and where it flips. Same args as evaluate_options_with_scenarios plus 'stress': {variable, variation, steps}.

    mcp-tool

    {
  "type": "object",
  "required": [
    "options",
    "criteria",
    "stress"
  ],
  "properties": {
    "inputs": {
      "type": "object"
    },
    "method": {
      "type": "string"
    },
    "stress": {
      "type": "object"
    },
    "horizon": {
      "type": "integer"
    },
    "options": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "criteria": {
      "type": "array",
      "items": {
        "type": "object"
      }
    },
    "template": {
      "type": "string"
    },
    "period_label": {
      "type": "string"
    }
  }
}
    arguments 40 lines
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card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

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_ record

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