# governance-platform MCP server Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof. ## Links - Registry page: https://www.getdrio.com/mcp/ai-geodesiclabs-governance-platform ## Install - Endpoint: https://app.geodesiclabs.ai/mcp - Auth: Not captured ## Setup notes - Remote endpoint: https://app.geodesiclabs.ai/mcp ## Tools - validate - 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. Endpoint: https://app.geodesiclabs.ai/mcp - validate_repair - 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. Endpoint: https://app.geodesiclabs.ai/mcp - create_blueprint - 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. Endpoint: https://app.geodesiclabs.ai/mcp - list_blueprints - List the Blueprints on this account with field/rule/constraint counts and mode. Use the returned workflow_name as 'blueprint' in validate. Endpoint: https://app.geodesiclabs.ai/mcp - repair - One-shot repair: return corrected values that would make failing data valid under the Blueprint. Use repair_path to see the steps instead. Endpoint: https://app.geodesiclabs.ai/mcp - check_blueprint_health - 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. Endpoint: https://app.geodesiclabs.ai/mcp - compare_semantic_equivalence - Compare two payloads under the dual-hash design: content_hash is invariant to field order and numeric formatting (5 vs '5.00'); semantic_hash additionally to field renaming. Verdicts: identical_content, same_structure_and_values_renamed_vocabulary, or semantically_different. Endpoint: https://app.geodesiclabs.ai/mcp - govern_inference - 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. Durably recorded; retrieve later with get_inference_trace. Endpoint: https://app.geodesiclabs.ai/mcp - get_inference_trace - Retrieve the durable audit trail for a governed generation: every recorded decision and its reasons. Endpoint: https://app.geodesiclabs.ai/mcp - recent_inference_decisions - Recent generation-governance decisions across all runs - what was approved, held, and escalated. Endpoint: https://app.geodesiclabs.ai/mcp - verify_certificate - 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. Endpoint: https://app.geodesiclabs.ai/mcp - profile_blueprint_robustness - 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. Endpoint: https://app.geodesiclabs.ai/mcp - forecast - Deterministic forward reasoning: from the current data state, generate and rank the valid next states reachable under the Blueprint's rules. Endpoint: https://app.geodesiclabs.ai/mcp - discover_patterns - 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. Endpoint: https://app.geodesiclabs.ai/mcp - repair_path - 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. Endpoint: https://app.geodesiclabs.ai/mcp - counterfactual - Run the same data under two rule sets and compare which future states remain valid - what-if analysis for rule changes. Endpoint: https://app.geodesiclabs.ai/mcp - analyze_anomaly - Explain whether a record fits the usual pattern for records like it, and which fields stand out. No Blueprint required. Endpoint: https://app.geodesiclabs.ai/mcp - create_chain - 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. Endpoint: https://app.geodesiclabs.ai/mcp - submit_chain_stage - 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. Endpoint: https://app.geodesiclabs.ai/mcp - handoff_audit - 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. Endpoint: https://app.geodesiclabs.ai/mcp - approve_rule - Promote a rule discovered by discover_patterns into Blueprint-ready form. Endpoint: https://app.geodesiclabs.ai/mcp - reject_rule - Reject a discovered candidate rule so it will not be promoted into a Blueprint. Pair with approve_rule after discover_patterns. Endpoint: https://app.geodesiclabs.ai/mcp - structural_types - 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. Endpoint: https://app.geodesiclabs.ai/mcp - decompose_failure - 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. Endpoint: https://app.geodesiclabs.ai/mcp - geometric_confidence - 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. Endpoint: https://app.geodesiclabs.ai/mcp - check_realization - 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. Endpoint: https://app.geodesiclabs.ai/mcp - check_drift - Check whether recent submissions still match the established pattern for this Blueprint. Returns a stability verdict and observation count. Endpoint: https://app.geodesiclabs.ai/mcp - authorize_execution - 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. Endpoint: https://app.geodesiclabs.ai/mcp - load_rule_pack - 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. Endpoint: https://app.geodesiclabs.ai/mcp - get_execution_trace - 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. Endpoint: https://app.geodesiclabs.ai/mcp - verify_replay - Verify two replay contracts represent the same deterministic execution: same input + same rules = same result, byte-identical. Mismatch fields localize the cause (data, rules, platform version, or trace). Use to prove a past decision reproduces today or that a migration changed nothing. Endpoint: https://app.geodesiclabs.ai/mcp - account_status - This account's plan, key usage, Blueprint counts, and the deployed platform build fingerprint (version, build, deployed). Endpoint: https://app.geodesiclabs.ai/mcp - delete_blueprint - Permanently delete a Blueprint and revoke its API keys. Irreversible; requires confirm=true. Account-level keys are unaffected. Endpoint: https://app.geodesiclabs.ai/mcp - update_blueprint - 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. Endpoint: https://app.geodesiclabs.ai/mcp - list_api_keys - List this account's API keys (masked) with their Blueprint bindings. Endpoint: https://app.geodesiclabs.ai/mcp - rotate_api_key - Replace an API key with a fresh one. The old key stops working immediately; the new key inherits its bindings. Endpoint: https://app.geodesiclabs.ai/mcp - delete_api_key - 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). Endpoint: https://app.geodesiclabs.ai/mcp ## Resources - geodesicai://docs/user-guide - The GeodesicAI user guide. Covers all tools, Blueprint design, Trajectory mode, and demo walkthroughs. Agents should read this on first connection to understand the platform's capabilities, semantics, and idioms before calling tools. MIME type: text/plain ## Prompts - blueprint_guide - Comprehensive guide to designing GeodesicAI Blueprints — governance contracts that define validation rules for structured data. - explain_result - Turn a validation result into plain English using the agent's own LLM. This is a prompt, not a tool — it returns a summarization template that the calling agent fills in with its own inference. GeodesicAI never runs an LLM on your tokens. Pass the JSON result from a prior validate / execute_task / repair call as `result_json`. Set `verbosity` to 'brief' (one sentence) or 'detailed' (full walkthrough). Arguments: result_json, verbosity ## Metadata - Owner: ai.geodesiclabs - Version: 1.2.0 - Runtime: Streamable Http - Transports: HTTP - License: Not captured - Language: Not captured - Stars: Not captured - Updated: Jul 27, 2026 - Source: https://registry.modelcontextprotocol.io