governance-platform

{
  "id": "aix_1ea49ba35a",
  "name": "governance-platform",
  "operator": "(unclaimed - source: registry-official · publisher: ai.geodesiclabs)",
  "description": "Pre-execution governance for AI agents. Deterministic PASS/FAIL/REVIEW verdicts, replayable proof.",
  "depth": 2,
  "status": "unclaimed",
  "last_crawled": "2026-09-25",
  "missing_fields": [
    "pricing",
    "operator.identity"
  ],
  "skills": [
    "invoice-parsing",
    "summarize-docs"
  ],
  "protocols": {
    "mcp": "https://app.geodesiclabs.ai/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": {
    "tasks_completed": null,
    "dispute_rate": null,
    "p95_latency_ms": 2085,
    "uptime_30d": 1,
    "onchain_volume_30d_usd": null
  },
  "aix_score": 47,
  "verification": {
    "identity": "none",
    "health": "probe/24h",
    "pricing": "unknown",
    "last_check": "2026-09-25T05:01:24.644Z"
  },
  "pricing_model": "unknown",
  "profile": {
    "mcp_server": "GeodesicAI",
    "mcp_version": "1.27.0",
    "tool_count": 37,
    "tools": [
      {
        "name": "validate",
        "description": "Validate structured data against a Blueprint's rules BEFORE the result\n    is used. Returns PASS, FAIL, or REVIEW with plain-language findings,\n    repair suggestions, a determinism hash, and a re-verifiable\n    certificate. Same input + same rules = same verdict, every time.\n    "
      },
      {
        "name": "validate_repair",
        "description": "Validate structured data against a Blueprint and, when it fails,\n    include repair suggestions (corrected values with the rule each fix is\n    based on) in the same call. Same verdicts as validate: PASS, FAIL, or\n    REVIEW, with reasons and proof.\n    "
      },
      {
        "name": "create_blueprint",
        "description": "Create a Blueprint - the governance contract validation runs against.\n\n    A Blueprint defines what correct means for your data: fields, the math\n    that must hold between them, and acceptable ranges. Start from\n    load_rule_pack or discover_patterns if you have no rules yet; invoke\n    the blueprint_guide prompt for the full rule/constraint reference.\n    Returns the new Blueprint's API key.\n    "
      },
      {
        "name": "list_blueprints",
        "description": "List the Blueprints on this account with field/rule/constraint counts\n    and mode. Use the returned workflow_name as 'blueprint' in validate.\n    "
      },
      {
        "name": "repair",
        "description": "One-shot repair: return corrected values that would make failing data\n    valid under the Blueprint. Use repair_path to see the steps instead.\n    "
      },
      {
        "name": "check_blueprint_health",
        "description": "Static pre-deploy analysis of a Blueprint's rule set. Returns a health\n    verdict - healthy, acceptable, fragile, rigid, split, brittle_islands,\n    or unsatisfiable - with advice, including joint conflicts pairwise\n    checks miss.\n    "
      },
      {
        "name": "compare_semantic_equivalence",
        "description": "Compare two payloads under the dual-hash design: content_hash is\n    content_hash normalizes field order and numeric formatting. Semantic\n    comparison preserves field roles; renaming requires an explicit bijection.\n    With supplied rules, scalar types and whitespace remain significant.\n    Structural similarity alone does not establish decision equivalence.\n    "
      },
      {
        "name": "govern_inference",
        "description": "Quality-govern an in-progress AI generation step BEFORE its output is\n    used (complements validate, which checks finished documents). Returns\n    an action - STOP, CONTINUE, REPAIR_REGION, REUSE_MOTIF, REVIEW,\n    ESCALATE - with a plain-language explanation. Structural scores do not\n    establish task correctness. Check safe_to_finalize and acceptance coverage.\n    Persistence success is reported; read traces in the same Blueprint namespace.\n    "
      },
      {
        "name": "get_inference_trace",
        "description": "Retrieve the durable audit trail for a governed generation: every\n    recorded decision and its reasons.\n    "
      },
      {
        "name": "recent_inference_decisions",
        "description": "Recent generation-governance decisions in this owner's Blueprint version.\n    "
      },
      {
        "name": "verify_certificate",
        "description": "Independently re-verify a validation certificate. Integrity mode checks\n    the hash chain; full mode (certificate + original data) recomputes\n    every attested rule from scratch - trust nothing, recheck everything.\n    "
      },
      {
        "name": "profile_blueprint_robustness",
        "description": "Sweep the Blueprint's numeric constraint bounds and report verdict\n    stability: the stable band, the scales where the verdict first flips,\n    and advice. Use before deploying bound changes.\n    "
      },
      {
        "name": "forecast",
        "description": "Deterministic forward reasoning: from the current data state, generate\n    and rank the valid next states reachable under the Blueprint's rules.\n    "
      },
      {
        "name": "discover_patterns",
        "description": "Learn candidate validation rules and structural document types from a\n    batch of your records, deterministically - no Blueprint required.\n    Promote results with approve_rule. Source data is not stored.\n    "
      },
      {
        "name": "repair_path",
        "description": "Find the shortest sequence of field changes taking invalid data to a\n    valid state, as an ordered path of intermediate states. Different from\n    repair (one-shot nearest fix): use repair_path to explain or audit the\n    fix, or compare alternative repairs.\n    "
      },
      {
        "name": "counterfactual",
        "description": "Run the same data under two rule sets and compare which future states\n    remain valid - what-if analysis for rule changes.\n    "
      },
      {
        "name": "analyze_anomaly",
        "description": "Explain whether a record fits the usual pattern for records like it,\n    and which fields stand out. No Blueprint required.\n    "
      },
      {
        "name": "create_chain",
        "description": "Create a multi-agent sequential chain: stages validate in order against\n    one Blueprint, repairs propagate forward, TTL bounds the run. Siblings:\n    submit_chain_stage advances the chain; handoff_audit verifies a\n    transition between stages. Returns chain_id.\n    "
      },
      {
        "name": "submit_chain_stage",
        "description": "Submit data for the chain's current stage; the platform validates it\n    and advances the chain if it passes. Response includes next-stage info\n    and accumulated repairs.\n    "
      },
      {
        "name": "handoff_audit",
        "description": "Audit a handoff between two chain stages: a context capsule of verified\n    facts from the prior stage, and (if proposed_data is given) a\n    compatibility verdict that catches fields mutated in transit. Siblings:\n    create_chain, submit_chain_stage.\n    "
      },
      {
        "name": "approve_rule",
        "description": "Promote a rule discovered by discover_patterns into Blueprint-ready\n    form.\n    "
      },
      {
        "name": "reject_rule",
        "description": "Reject a discovered candidate rule so it will not be promoted into a\n    Blueprint. Pair with approve_rule after discover_patterns.\n    "
      },
      {
        "name": "structural_types",
        "description": "Retrieve the document categories a discover_patterns session identified\n    (counts, distinguishing fields, domain hints). Read-only; returns\n    status=no_session if discovery has not run for this namespace.\n    "
      },
      {
        "name": "decompose_failure",
        "description": "Split the error between original and corrected values into direct rule\n    violations, boundary violations, and systemic structural error, with\n    per-field contributions. Use with a known-correct version to diff\n    against; use analyze_anomaly when you only have the suspicious payload.\n    Diagnostics-tier tool.\n    "
      },
      {
        "name": "geometric_confidence",
        "description": "Summarize an already-computed state_vector into a confidence level\n    (high/medium/low) with a recommendation. Post-hoc digest - use\n    analyze_anomaly or check_drift for fresh analysis of raw data.\n    "
      }
    ],
    "profiled_at": "2026-09-25T05:01:24.644Z"
  },
  "unreachable": false
}