_ registry / mcp http-sse · checked 34m ago

GeodesicAI

https://app.geodesiclabs.ai

Registry code: 4ca490a1bc95469e

api record

GeodesicAI is pre-execution governance for AI agents. Other tools do something; GeodesicAI decides whether it should happen — with deterministic, replayable verdicts, never a guess.

GOVERNANCE (the core) — Validate agent outputs and authorize actions against Blueprint rules BEFORE they execute. Catches math errors, field inconsistencies, policy-limit breaches, and constraint violations. Every check returns PASS, FAIL, or REVIEW with plain-language reasons, repair suggestions, a tamper-evident determinism hash, and a proof certificate you can re-verify independently. Same input, same rules,…

endpoint
https://app.geodesiclabs.ai/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
99.3%

90 days 99.3%· all time 99.4%

latency
436ms

last good check

priced tools
0

of 37 tools

_ answered our checks, 90 days 142 checks · signed record
_ what it is for
topic
ai & agents evaluation & judging
used for
  • validate agent output against rules
  • authorize an agent action before it runs
  • learn validation rules from records
  • detect drift in submitted data
  • suggest repairs for invalid data
takes → gives
data, text → data
tools
17 reads8 changes data
_ 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 37 tools
37 never probed 0 of 37 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.

  • list_blueprints reads unknown never probed

    List the Blueprints on this account with field/rule/constraint counts and mode. Use the returned workflow_name as 'blueprint' in validate.

    mcp-tool

    {
  "type": "object",
  "title": "list_blueprintsArguments",
  "required": [
    "api_key"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    }
  }
}
    arguments 14 lines
  • list_api_keys unknown never probed

    List this account's API keys (masked) with their Blueprint bindings.

    mcp-tool

    {
  "type": "object",
  "title": "list_api_keysArguments",
  "required": [
    "api_key"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    }
  }
}
    arguments 14 lines
  • compare_semantic_equivalence reads unknown never probed

    Compare two payloads under the dual-hash design: content_hash is content_hash normalizes field order and numeric formatting. Semantic comparison preserves field roles; renaming requires an explicit bijection. With supplied rules, scalar types and whitespace remain significant. Structural similarity alone does not establish decision equivalence.

    mcp-tool

    {
  "type": "object",
  "title": "compare_semantic_equivalenceArguments",
  "required": [
    "api_key",
    "payload_a",
    "payload_b"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "rules_a": {
      "type": "array",
      "items": {},
      "title": "Rules A",
      "default": null,
      "description": "Derivation rules for A"
    },
    "rules_b": {
      "type": "array",
      "items": {},
      "title": "Rules B",
      "default": null,
      "description": "Derivation rules for B"
    },
    "payload_a": {
      "type": "object",
      "title": "Payload A",
      "description": "First structured payload (arbitrary JSON object)",
      "additionalProperties": true
    },
    "payload_b": {
      "type": "object",
      "title": "Payload B",
      "description": "Second structured payload to compare against payload_a",
      "additionalProperties": true
    },
    "constraints_a": {
      "type": "array",
      "items": {},
      "title": "Constraints A",
      "default": null,
      "description": "Formal constraints for A"
    },
    "constraints_b": {
      "type": "array",
      "items": {},
      "title": "Constraints B",
      "default": null,
      "description": "Formal constraints for B"
    },
    "field_mapping": {
      "type": "object",
      "title": "Field Mapping",
      "default": null,
      "description": "Explicit one-to-one field renaming from A to B",
      "additionalProperties": true
    }
  }
}
    arguments 63 lines
  • update_blueprint changes data unknown never probed

    Update an existing Blueprint in place. Only passed fields change; pass [] to clear a list. workflow_name cannot be renamed and existing API keys keep working. Different from create_blueprint: modifies an existing Blueprint, mints no new key.

    mcp-tool

    {
  "type": "object",
  "title": "update_blueprintArguments",
  "required": [
    "api_key",
    "workflow_name"
  ],
  "properties": {
    "mode": {
      "type": "string",
      "title": "Mode",
      "default": null,
      "description": "New mode: observe or enforce; omit to keep current"
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "require_math": {
      "type": "boolean",
      "title": "Require Math",
      "default": null,
      "description": "Validate mathematical relationships"
    },
    "customer_name": {
      "type": "string",
      "title": "Customer Name",
      "default": null,
      "description": "Organization or project name (also used for storage folder naming)"
    },
    "workflow_name": {
      "type": "string",
      "title": "Workflow Name",
      "description": "Unique Blueprint identifier; the value passed as 'blueprint' in validate"
    },
    "derived_fields": {
      "type": "array",
      "items": {},
      "title": "Derived Fields",
      "default": null,
      "description": "Field names the platform computes from other fields, e.g. ['subtotal','total']"
    },
    "semantic_checks": {
      "type": "array",
      "items": {},
      "title": "Semantic Checks",
      "default": null,
      "description": "Domain-specific semantic check objects"
    },
    "derivation_rules": {
      "type": "array",
      "items": {},
      "title": "Derivation Rules",
      "default": null,
      "description": "Math rules as objects. Types: add, subtract, multiply, divide, round, copy, sum (multi-operand), items_multiply, items_sum. Each needs 'type' plus its fields; see the blueprint_guide prompt"
    },
    "extracted_fields": {
      "type": "array",
      "items": {},
      "title": "Extracted Fields",
      "default": null,
      "description": "Field names the agent extracts from source data, e.g. ['vendor','qty','unit_cost']"
    },
    "require_coherence": {
      "type": "boolean",
      "title": "Require Coherence",
      "default": null,
      "description": "Check cross-field plausibility"
    },
    "formal_constraints": {
      "type": "array",
      "items": {},
      "title": "Formal Constraints",
      "default": null,
      "description": "Constraint objects. Types incl. magnitude_anchor {field,min,max}, relative_anchor {field,reference_field,ratio_min,ratio_max}, max_action_threshold {field,threshold,on_violation}, required_fields {fields}, equals, range, in_set, regex_match, items_magnitude_anchor; see the blueprint_guide prompt"
    },
    "require_provenance": {
      "type": "boolean",
      "title": "Require Provenance",
      "default": null,
      "description": "Require extraction source locations for fields"
    },
    "require_consistency": {
      "type": "boolean",
      "title": "Require Consistency",
      "default": null,
      "description": "Check internal field consistency"
    },
    "enable_drift_tracking": {
      "type": "boolean",
      "title": "Enable Drift Tracking",
      "default": null,
      "description": "Track pattern stability across batches"
    },
    "require_high_assurance": {
      "type": "boolean",
      "title": "Require High Assurance",
      "default": null,
      "description": "Strictest mode: every check must pass"
    },
    "enable_anomaly_detection": {
      "type": "boolean",
      "title": "Enable Anomaly Detection",
      "default": null,
      "description": "Flag records that break no rules but do not fit the reference pattern"
    }
  }
}
    arguments 109 lines
  • submit_chain_stage changes data unknown never probed

    Submit data for the chain's current stage; the platform validates it and advances the chain if it passes. Response includes next-stage info and accumulated repairs.

    mcp-tool

    {
  "type": "object",
  "title": "submit_chain_stageArguments",
  "required": [
    "api_key",
    "chain_id",
    "stage",
    "structured_data"
  ],
  "properties": {
    "stage": {
      "type": "string",
      "title": "Stage",
      "description": "Stage name to submit for (must be the chain's current stage)"
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "chain_id": {
      "type": "string",
      "title": "Chain Id",
      "description": "Chain identifier returned by create_chain"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 33 lines
  • geometric_confidence reads unknown never probed

    Summarize an already-computed state_vector into a confidence level (high/medium/low) with a recommendation. Post-hoc digest - use analyze_anomaly or check_drift for fresh analysis of raw data.

    mcp-tool

    {
  "type": "object",
  "title": "geometric_confidenceArguments",
  "required": [
    "api_key",
    "state_vector"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "state_vector": {
      "type": "object",
      "title": "State Vector",
      "description": "state_vector object from a prior validate or get_execution_trace result",
      "additionalProperties": true
    }
  }
}
    arguments 21 lines
  • check_drift reads unknown never probed

    Check whether recent submissions still match the established pattern for this Blueprint. Returns a stability verdict and observation count.

    mcp-tool

    {
  "type": "object",
  "title": "check_driftArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 27 lines
  • delete_blueprint changes data unknown never probed

    Permanently delete a Blueprint and revoke its API keys. Irreversible; requires confirm=true. Account-level keys are unaffected.

    mcp-tool

    {
  "type": "object",
  "title": "delete_blueprintArguments",
  "required": [
    "api_key",
    "workflow_name"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "confirm": {
      "type": "boolean",
      "title": "Confirm",
      "default": false,
      "description": "Must be true to confirm this irreversible action"
    },
    "workflow_name": {
      "type": "string",
      "title": "Workflow Name",
      "description": "Blueprint to delete; its API keys are revoked"
    }
  }
}
    arguments 26 lines
  • rotate_api_key changes data unknown never probed

    Replace an API key with a fresh one. The old key stops working immediately; the new key inherits its bindings.

    mcp-tool

    {
  "type": "object",
  "title": "rotate_api_keyArguments",
  "required": [
    "api_key",
    "key_to_rotate"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "key_to_rotate": {
      "type": "string",
      "title": "Key To Rotate",
      "description": "The gai_ key to rotate; it stops working immediately"
    }
  }
}
    arguments 20 lines
  • validate unknown never probed

    Validate structured data against a Blueprint's rules BEFORE the result is used. Returns PASS, FAIL, or REVIEW with plain-language findings, repair suggestions, a determinism hash, and a re-verifiable certificate. Same input + same rules = same verdict, every time.

    mcp-tool

    {
  "type": "object",
  "title": "validateArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 27 lines
  • create_blueprint unknown never probed

    Create a Blueprint - the governance contract validation runs against. A Blueprint defines what correct means for your data: fields, the math that must hold between them, and acceptable ranges. Start from load_rule_pack or discover_patterns if you have no rules yet; invoke the blueprint_guide prompt for the full rule/constraint reference. Returns the new Blueprint's API key.

    mcp-tool

    {
  "type": "object",
  "title": "create_blueprintArguments",
  "required": [
    "api_key",
    "customer_name",
    "workflow_name"
  ],
  "properties": {
    "mode": {
      "enum": [
        "observe",
        "enforce"
      ],
      "type": "string",
      "title": "Mode",
      "default": "observe",
      "description": "observe: platform checks the agent's work; enforce: platform computes derived fields itself"
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "require_math": {
      "type": "boolean",
      "title": "Require Math",
      "default": true,
      "description": "Validate mathematical relationships"
    },
    "customer_name": {
      "type": "string",
      "title": "Customer Name",
      "description": "Organization or project name (also used for storage folder naming)"
    },
    "workflow_name": {
      "type": "string",
      "title": "Workflow Name",
      "description": "Unique Blueprint identifier; the value passed as 'blueprint' in validate"
    },
    "derived_fields": {
      "type": "array",
      "items": {},
      "title": "Derived Fields",
      "default": null,
      "description": "Field names the platform computes from other fields, e.g. ['subtotal','total']"
    },
    "semantic_checks": {
      "type": "array",
      "items": {},
      "title": "Semantic Checks",
      "default": null,
      "description": "Domain-specific semantic check objects"
    },
    "derivation_rules": {
      "type": "array",
      "items": {},
      "title": "Derivation Rules",
      "default": null,
      "description": "Math rules as objects. Types: add, subtract, multiply, divide, round, copy, sum (multi-operand), items_multiply, items_sum. Each needs 'type' plus its fields; see the blueprint_guide prompt"
    },
    "extracted_fields": {
      "type": "array",
      "items": {},
      "title": "Extracted Fields",
      "default": null,
      "description": "Field names the agent extracts from source data, e.g. ['vendor','qty','unit_cost']"
    },
    "require_coherence": {
      "type": "boolean",
      "title": "Require Coherence",
      "default": true,
      "description": "Check cross-field plausibility"
    },
    "formal_constraints": {
      "type": "array",
      "items": {},
      "title": "Formal Constraints",
      "default": null,
      "description": "Constraint objects. Types incl. magnitude_anchor {field,min,max}, relative_anchor {field,reference_field,ratio_min,ratio_max}, max_action_threshold {field,threshold,on_violation}, required_fields {fields}, equals, range, in_set, regex_match, items_magnitude_anchor; see the blueprint_guide prompt"
    },
    "require_provenance": {
      "type": "boolean",
      "title": "Require Provenance",
      "default": false,
      "description": "Require extraction source locations for fields"
    },
    "require_consistency": {
      "type": "boolean",
      "title": "Require Consistency",
      "default": true,
      "description": "Check internal field consistency"
    },
    "enable_drift_tracking": {
      "type": "boolean",
      "title": "Enable Drift Tracking",
      "default": false,
      "description": "Track pattern stability across batches"
    },
    "require_high_assurance": {
      "type": "boolean",
      "title": "Require High Assurance",
      "default": false,
      "description": "Strictest mode: every check must pass"
    },
    "enable_anomaly_detection": {
      "type": "boolean",
      "title": "Enable Anomaly Detection",
      "default": false,
      "description": "Flag records that break no rules but do not fit the reference pattern"
    }
  }
}
    arguments 113 lines
  • repair unknown never probed

    One-shot repair: return corrected values that would make failing data valid under the Blueprint. Use repair_path to see the steps instead.

    mcp-tool

    {
  "type": "object",
  "title": "repairArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": null,
      "description": "Blueprint name (workflow_name) to use"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    },
    "derivation_rules": {
      "type": "array",
      "items": {},
      "title": "Derivation Rules",
      "default": null,
      "description": "Math rules as objects. Types: add, subtract, multiply, divide, round, copy, sum (multi-operand), items_multiply, items_sum. Each needs 'type' plus its fields; see the blueprint_guide prompt"
    },
    "formal_constraints": {
      "type": "array",
      "items": {},
      "title": "Formal Constraints",
      "default": null,
      "description": "Constraint objects. Types incl. magnitude_anchor {field,min,max}, relative_anchor {field,reference_field,ratio_min,ratio_max}, max_action_threshold {field,threshold,on_violation}, required_fields {fields}, equals, range, in_set, regex_match, items_magnitude_anchor; see the blueprint_guide prompt"
    }
  }
}
    arguments 41 lines
  • govern_inference reads unknown never probed

    Quality-govern an in-progress AI generation step BEFORE its output is used (complements validate, which checks finished documents). Returns an action - STOP, CONTINUE, REPAIR_REGION, REUSE_MOTIF, REVIEW, ESCALATE - with a plain-language explanation. Structural scores do not establish task correctness. Check safe_to_finalize and acceptance coverage. Persistence success is reported; read traces in the same Blueprint namespace.

    mcp-tool

    {
  "type": "object",
  "title": "govern_inferenceArguments",
  "required": [
    "api_key",
    "task_type",
    "payload",
    "inference_id"
  ],
  "properties": {
    "source": {
      "type": "string",
      "title": "Source",
      "default": "mcp",
      "description": "Free-form caller label recorded for audit"
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "payload": {
      "type": "object",
      "title": "Payload",
      "description": "Task-type payload: generative_text {text,...}; retrieval {query,candidates}; generic {features}",
      "additionalProperties": true
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Owned Blueprint namespace for the trace"
    },
    "task_type": {
      "enum": [
        "generative_text",
        "retrieval",
        "generic"
      ],
      "type": "string",
      "title": "Task Type",
      "description": "Kind of generation step being governed"
    },
    "step_index": {
      "type": "integer",
      "title": "Step Index",
      "default": 0,
      "description": "Step number within this generation (0-based)"
    },
    "constraints": {
      "type": "object",
      "title": "Constraints",
      "default": null,
      "description": "Optional governance constraint config object",
      "additionalProperties": true
    },
    "inference_id": {
      "type": "string",
      "title": "Inference Id",
      "description": "Caller-chosen ID grouping the steps of one generation"
    }
  }
}
    arguments 63 lines
  • get_inference_trace unknown never probed

    Retrieve the durable audit trail for a governed generation: every recorded decision and its reasons.

    mcp-tool

    {
  "type": "object",
  "title": "get_inference_traceArguments",
  "required": [
    "api_key",
    "inference_id"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint namespace used when recording the trace"
    },
    "inference_id": {
      "type": "string",
      "title": "Inference Id",
      "description": "Caller-chosen ID grouping the steps of one generation"
    },
    "blueprint_version": {
      "type": "string",
      "title": "Blueprint Version",
      "default": "",
      "description": "Optional historical blueprint_version hash returned by govern_inference"
    }
  }
}
    arguments 32 lines
  • recent_inference_decisions reads unknown never probed

    Recent generation-governance decisions in this owner's Blueprint version.

    mcp-tool

    {
  "type": "object",
  "title": "recent_inference_decisionsArguments",
  "required": [
    "api_key"
  ],
  "properties": {
    "limit": {
      "type": "integer",
      "title": "Limit",
      "default": 25,
      "description": "Maximum rows to return"
    },
    "action": {
      "type": "string",
      "title": "Action",
      "default": "",
      "description": "Optional action filter (STOP, CONTINUE, REVIEW, ...)"
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint namespace used when recording the trace"
    },
    "blueprint_version": {
      "type": "string",
      "title": "Blueprint Version",
      "default": "",
      "description": "Optional historical blueprint_version hash returned by govern_inference"
    }
  }
}
    arguments 38 lines
  • validate_repair reads unknown never probed

    Validate structured data against a Blueprint and, when it fails, include repair suggestions (corrected values with the rule each fix is based on) in the same call. Same verdicts as validate: PASS, FAIL, or REVIEW, with reasons and proof.

    mcp-tool

    {
  "type": "object",
  "title": "validate_repairArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 27 lines
  • check_blueprint_health reads unknown never probed

    Static pre-deploy analysis of a Blueprint's rule set. Returns a health verdict - healthy, acceptable, fragile, rigid, split, brittle_islands, or unsatisfiable - with advice, including joint conflicts pairwise checks miss.

    mcp-tool

    {
  "type": "object",
  "title": "check_blueprint_healthArguments",
  "required": [
    "api_key"
  ],
  "properties": {
    "config": {
      "type": "object",
      "title": "Config",
      "default": null,
      "description": "Raw blueprint config with derivation_rules and formal_constraints (used when 'blueprint' is not given)",
      "additionalProperties": true
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "",
      "description": "Blueprint name (workflow_name) to use"
    }
  }
}
    arguments 27 lines
  • forecast unknown never probed

    Deterministic forward reasoning: from the current data state, generate and rank the valid next states reachable under the Blueprint's rules.

    mcp-tool

    {
  "type": "object",
  "title": "forecastArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "rank_by": {
      "enum": [
        "drift",
        "confidence",
        "shortest",
        "risk"
      ],
      "type": "string",
      "title": "Rank By",
      "default": "drift",
      "description": "Ranking criterion for returned paths"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "max_depth": {
      "type": "integer",
      "title": "Max Depth",
      "default": 3,
      "description": "Search depth, 1-10"
    },
    "max_branches": {
      "type": "integer",
      "title": "Max Branches",
      "default": 5,
      "description": "Branches per step, 1-10"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 51 lines
  • check_realization unknown never probed

    Structural realization analysis of a payload against the Blueprint's reference configuration (requires a 'realization' block; otherwise status=skipped). Diagnostics-tier tool; prefer validate or analyze_anomaly for standard checks.

    mcp-tool

    {
  "type": "object",
  "title": "check_realizationArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 27 lines
  • analyze_anomaly reads unknown never probed

    Explain whether a record fits the usual pattern for records like it, and which fields stand out. No Blueprint required.

    mcp-tool

    {
  "type": "object",
  "title": "analyze_anomalyArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Discovery namespace used by discover_patterns"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 27 lines
  • verify_certificate reads unknown never probed

    Independently re-verify a validation certificate. Integrity mode checks the hash chain; full mode (certificate + original data) recomputes every attested rule from scratch - trust nothing, recheck everything.

    mcp-tool

    {
  "type": "object",
  "title": "verify_certificateArguments",
  "required": [
    "api_key",
    "certificate"
  ],
  "properties": {
    "data": {
      "type": "object",
      "title": "Data",
      "default": null,
      "description": "Original payload for full re-verification; omit for integrity-only mode",
      "additionalProperties": true
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "certificate": {
      "type": "object",
      "title": "Certificate",
      "description": "The certificate object from a prior validation response",
      "additionalProperties": true
    },
    "derivation_rules": {
      "type": "array",
      "items": {},
      "title": "Derivation Rules",
      "default": null,
      "description": "Rule list for independent attestation recomputation (optional)"
    },
    "formal_constraints": {
      "type": "array",
      "items": {},
      "title": "Formal Constraints",
      "default": null,
      "description": "Optional constraints to match against the committed bundle"
    }
  }
}
    arguments 42 lines
  • profile_blueprint_robustness unknown never probed

    Sweep the Blueprint's numeric constraint bounds and report verdict stability: the stable band, the scales where the verdict first flips, and advice. Use before deploying bound changes.

    mcp-tool

    {
  "type": "object",
  "title": "profile_blueprint_robustnessArguments",
  "required": [
    "api_key"
  ],
  "properties": {
    "config": {
      "type": "object",
      "title": "Config",
      "default": null,
      "description": "Raw blueprint config to profile (used when 'blueprint' is not given)",
      "additionalProperties": true
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "",
      "description": "Blueprint name (workflow_name) to use"
    }
  }
}
    arguments 27 lines
  • discover_patterns reads unknown never probed

    Learn candidate validation rules and structural document types from a batch of your records, deterministically - no Blueprint required. Promote results with approve_rule. Source data is not stored.

    mcp-tool

    {
  "type": "object",
  "title": "discover_patternsArguments",
  "required": [
    "api_key",
    "documents"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "documents": {
      "type": "array",
      "items": {},
      "title": "Documents",
      "description": "List of structured records (objects) to analyze"
    }
  }
}
    arguments 27 lines
  • repair_path reads unknown never probed

    Find the shortest sequence of field changes taking invalid data to a valid state, as an ordered path of intermediate states. Different from repair (one-shot nearest fix): use repair_path to explain or audit the fix, or compare alternative repairs.

    mcp-tool

    {
  "type": "object",
  "title": "repair_pathArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "rank_by": {
      "enum": [
        "shortest",
        "drift",
        "confidence",
        "risk"
      ],
      "type": "string",
      "title": "Rank By",
      "default": "shortest",
      "description": "Ranking criterion for returned paths"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "max_depth": {
      "type": "integer",
      "title": "Max Depth",
      "default": 4,
      "description": "Search depth, 1-10"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 45 lines
  • counterfactual reads unknown never probed

    Run the same data under two rule sets and compare which future states remain valid - what-if analysis for rule changes.

    mcp-tool

    {
  "type": "object",
  "title": "counterfactualArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "rules_b": {
      "type": "array",
      "items": {},
      "title": "Rules B",
      "default": null,
      "description": "Alternative derivation rules (rule set B)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "constraints_b": {
      "type": "array",
      "items": {},
      "title": "Constraints B",
      "default": null,
      "description": "Alternative constraints (rule set B)"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 41 lines
  • create_chain changes data unknown never probed

    Create a multi-agent sequential chain: stages validate in order against one Blueprint, repairs propagate forward, TTL bounds the run. Siblings: submit_chain_stage advances the chain; handoff_audit verifies a transition between stages. Returns chain_id.

    mcp-tool

    {
  "type": "object",
  "title": "create_chainArguments",
  "required": [
    "api_key",
    "blueprint",
    "stages"
  ],
  "properties": {
    "ttl": {
      "type": "integer",
      "title": "Ttl",
      "default": 3600,
      "description": "Chain timeout in seconds; stages cannot advance after expiry"
    },
    "stages": {
      "type": "array",
      "items": {},
      "title": "Stages",
      "description": "Stage definitions, e.g. [{'stage_name':'extract','agent_name':'PDF Agent'}]; minimum 2"
    },
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "description": "Blueprint governing all stages of the chain"
    }
  }
}
    arguments 33 lines
  • handoff_audit reads unknown never probed

    Audit a handoff between two chain stages: a context capsule of verified facts from the prior stage, and (if proposed_data is given) a compatibility verdict that catches fields mutated in transit. Siblings: create_chain, submit_chain_stage.

    mcp-tool

    {
  "type": "object",
  "title": "handoff_auditArguments",
  "required": [
    "api_key",
    "chain_id",
    "from_stage",
    "to_stage"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "chain_id": {
      "type": "string",
      "title": "Chain Id",
      "description": "Chain identifier returned by create_chain"
    },
    "to_stage": {
      "type": "string",
      "title": "To Stage",
      "description": "Stage about to start (agent B)"
    },
    "from_stage": {
      "type": "string",
      "title": "From Stage",
      "description": "Completed stage name (agent A)"
    },
    "proposed_data": {
      "type": "object",
      "title": "Proposed Data",
      "default": null,
      "description": "Data agent B intends to submit; checked for mutation against agent A's verified fields",
      "additionalProperties": true
    }
  }
}
    arguments 39 lines
  • approve_rule changes data unknown never probed

    Promote a rule discovered by discover_patterns into Blueprint-ready form.

    mcp-tool

    {
  "type": "object",
  "title": "approve_ruleArguments",
  "required": [
    "api_key",
    "rule_id"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "rule_id": {
      "type": "string",
      "title": "Rule Id",
      "description": "Discovered rule ID from discover_patterns"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    }
  }
}
    arguments 26 lines
  • reject_rule changes data unknown never probed

    Reject a discovered candidate rule so it will not be promoted into a Blueprint. Pair with approve_rule after discover_patterns.

    mcp-tool

    {
  "type": "object",
  "title": "reject_ruleArguments",
  "required": [
    "api_key",
    "rule_id"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "rule_id": {
      "type": "string",
      "title": "Rule Id",
      "description": "Discovered rule ID from discover_patterns"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    }
  }
}
    arguments 26 lines
  • structural_types unknown never probed

    Retrieve the document categories a discover_patterns session identified (counts, distinguishing fields, domain hints). Read-only; returns status=no_session if discovery has not run for this namespace.

    mcp-tool

    {
  "type": "object",
  "title": "structural_typesArguments",
  "required": [
    "api_key"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    }
  }
}
    arguments 20 lines
  • decompose_failure reads unknown never probed

    Split the error between original and corrected values into direct rule violations, boundary violations, and systemic structural error, with per-field contributions. Use with a known-correct version to diff against; use analyze_anomaly when you only have the suspicious payload. Diagnostics-tier tool.

    mcp-tool

    {
  "type": "object",
  "title": "decompose_failureArguments",
  "required": [
    "api_key",
    "original_values",
    "corrected_values"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": null,
      "description": "Load rules from this Blueprint instead of passing them inline"
    },
    "original_values": {
      "type": "object",
      "title": "Original Values",
      "description": "Original numeric field values as {field: number}",
      "additionalProperties": true
    },
    "corrected_values": {
      "type": "object",
      "title": "Corrected Values",
      "description": "Corrected/expected numeric field values as {field: number}",
      "additionalProperties": true
    },
    "derivation_rules": {
      "type": "array",
      "items": {},
      "title": "Derivation Rules",
      "default": null,
      "description": "Math rules as objects. Types: add, subtract, multiply, divide, round, copy, sum (multi-operand), items_multiply, items_sum. Each needs 'type' plus its fields; see the blueprint_guide prompt"
    },
    "formal_constraints": {
      "type": "array",
      "items": {},
      "title": "Formal Constraints",
      "default": null,
      "description": "Constraint objects. Types incl. magnitude_anchor {field,min,max}, relative_anchor {field,reference_field,ratio_min,ratio_max}, max_action_threshold {field,threshold,on_violation}, required_fields {fields}, equals, range, in_set, regex_match, items_magnitude_anchor; see the blueprint_guide prompt"
    }
  }
}
    arguments 48 lines
  • authorize_execution unknown never probed

    Go/no-go for a real-world action (payment, filing, API write): runs full validation, then the Blueprint's execution gate. authorized=true only on PASS; REVIEW means do not proceed automatically. Different from validate: validate asks is this data correct, authorize_execution asks should this action happen.

    mcp-tool

    {
  "type": "object",
  "title": "authorize_executionArguments",
  "required": [
    "api_key",
    "structured_data",
    "blueprint"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "description": "Blueprint name (workflow_name) to use"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 27 lines
  • load_rule_pack changes data unknown never probed

    Load a prebuilt Blueprint template (invoices, timecards, legal, POs, claims). Call without pack_id to list packs; then create_blueprint to save a customized copy.

    mcp-tool

    {
  "type": "object",
  "title": "load_rule_packArguments",
  "required": [
    "api_key"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "pack_id": {
      "type": "string",
      "title": "Pack Id",
      "default": null,
      "description": "Rule pack ID; omit to list available packs"
    }
  }
}
    arguments 20 lines
  • get_execution_trace reads unknown never probed

    Run validation and return the per-node execution trace (node names, deterministic flags, timing) plus the verdict and determinism hash. Use validate for normal operation; this is for debugging and audit preparation.

    mcp-tool

    {
  "type": "object",
  "title": "get_execution_traceArguments",
  "required": [
    "api_key",
    "structured_data"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "blueprint": {
      "type": "string",
      "title": "Blueprint",
      "default": "default",
      "description": "Blueprint name (workflow_name) to use"
    },
    "structured_data": {
      "type": "object",
      "title": "Structured Data",
      "description": "The document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked",
      "additionalProperties": true
    }
  }
}
    arguments 27 lines
  • verify_replay unknown never probed

    Check replay commitment integrity and compare recorded execution components. Version 4 includes reference context and the final result/status. A match compares commitments; this tool does not rerun the workflow or reconstruct historical reference populations, and does not prove factual correctness.

    mcp-tool

    {
  "type": "object",
  "title": "verify_replayArguments",
  "required": [
    "api_key",
    "contract_a",
    "contract_b"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "contract_a": {
      "type": "object",
      "title": "Contract A",
      "description": "replay_contract object from one execution",
      "additionalProperties": true
    },
    "contract_b": {
      "type": "object",
      "title": "Contract B",
      "description": "replay_contract object to compare against contract_a",
      "additionalProperties": true
    }
  }
}
    arguments 28 lines
  • account_status reads unknown never probed

    This account's plan, key usage, Blueprint counts, and the deployed platform build fingerprint (version, build, deployed).

    mcp-tool

    {
  "type": "object",
  "title": "account_statusArguments",
  "required": [
    "api_key"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    }
  }
}
    arguments 14 lines
  • delete_api_key unknown never probed

    Permanently delete one of the caller's API keys. DESTRUCTIVE — agents using the deleted key will receive auth errors immediately. The Blueprint a key was tied to (if any) is NOT affected; only the credential is revoked. To delete a Blueprint and all its keys, use delete_blueprint. The target key can be specified two ways: - As the full key string (gai_...). - As a key_id (SHA-256 hash from list_api_keys).

    mcp-tool

    {
  "type": "object",
  "title": "delete_api_keyArguments",
  "required": [
    "api_key",
    "key_to_delete"
  ],
  "properties": {
    "api_key": {
      "type": "string",
      "title": "Api Key",
      "description": "GeodesicAI API key (gai_...)"
    },
    "confirm": {
      "type": "boolean",
      "title": "Confirm",
      "default": false,
      "description": "Must be true to confirm this irreversible action"
    },
    "key_to_delete": {
      "type": "string",
      "title": "Key To Delete",
      "description": "The gai_ key to delete"
    }
  }
}
    arguments 26 lines
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Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
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