"Observable" matching MCP tools:

• guides.get

  AI Design Blueprint Doctrine

  Get a full application guide by its stable slug (e.g. 'security-application', 'observable-evaluation'). Returns sections, action items, and linked principles. Use this when you already have the guide slug from guides.list or guides.search. Prefer guides.search when the user describes a topic in natural language; prefer guides.list when you need the full inventory.

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• guides.get

  AI Design Blueprint

  Get a full application guide by its stable slug (e.g. 'security-application', 'observable-evaluation'). Returns sections, action items, and linked principles. Use this when you already have the guide slug from guides.list or guides.search. Prefer guides.search when the user describes a topic in natural language; prefer guides.list when you need the full inventory.

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• goal-add-criterion

  Planner (proof-of-done)

  Append an acceptance criterion to a goal. The text must describe an observable check over an artifact (e.g. "GET /api/health returns 200 with {status:ok}"), not a subjective approval. Grove mode: AC can only be added while goal is in backlog (frozen once started), quality linter blocks high-severity issues. Standard mode: AC editable until goal is closed, linter is advisory. Returns criterion id, position, text, and any quality findings.

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• get_smart_money_flow

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Composite flow score on [-100, +100] aggregating insider transactions, 13F institutional Δ-shares vs the prior quarter, and SC 13D/13G blockholder changes over a lookback window. Each component normalised independently, then combined with configurable weights (default: institutional 0.4, blockholder 0.4, insider 0.2). Returns per-component attribution so an agent can see WHY the score is what it is — not just the headline number. NOTE: the institutional component is a QoQ share-change signal computed over the top-5 13F filers on a MATCHED current-vs-prior basis (a filer only counts when its prior-quarter book is observable), NOT the issuer's complete institutional book — treat the score as a directional signal, not an exact flow. `coverage.coverage_confidence` (0–1) reports how much of that basis had a real prior quarter; when it is 0 the institutional component is forced to 0 so a 13F ingestion gap can never surface as a false max-conviction buy. See the `coverage` block for holder coverage + staleness. The score is a unitless composite, not a dollar figure. Institutional tier only.

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• verify_replay

  governance-platform

  Verify that two execution replay contracts represent the same deterministic result. This is the programmatic proof of GeodesicAI's core promise: same input + same rules = same result, every time. Given two replay contracts (e.g. from the original execution and a re-run), this tool compares all component hashes and reports whether the executions are byte-identical. Use this to: - Prove to an auditor that a decision from March 3rd matches a re-run today. - Detect when a rule change has altered execution behavior (input hash matches but canonical trace hash differs → the rules diverged). - Confirm a Blueprint migration didn't change any observable outcomes. Args: api_key: GeodesicAI API key (starts with gai_) contract_a: A replay contract dict (the `replay_contract` field from a prior validate/execute_task response) contract_b: Another replay contract dict to compare against contract_a Returns: replay_match: bool — True if the top-level replay_hash matches (fully identical) contract_version_match: bool matches: dict of field_name → value, for every field that agreed mismatches: dict of field_name → {expected, actual}, for every field that disagreed summary: plain-English one-liner describing the result Interpretation of mismatches: - data_merkle_root: the two runs were fed different data (data_field_diff localizes exactly which fields changed) - rules_hash: the Blueprint's rules/constraints/thresholds differ - template_version: the Blueprint was upgraded between runs - solver_registry_hash: the platform itself changed between runs - canonical_trace_hash: same inputs and rules but different execution path (should never happen under determinism; indicates a platform bug) - graph_hash: DAG topology changed between runs

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• check_agent_reputation

  TensorFeed

  Look up an agent's public reputation card from the TensorFeed Agent Reputation Bureau. Takes a wallet (0x + 40 hex) OR a token_prefix (first 16 chars of a tf_live_ bearer). Returns the full ReputationCard: composite + sub-metric ranks (reliability, spend, activity, streak), trust grade A through F, public flags (new_wallet, spend_spike, claim_disputed, etc), wallet age, first_seen, last_active, ofac_clean, banned + ban_reason if applicable. Cards rebuild daily at 04:50 UTC from TF's own observable telemetry. Returns ok=false with status=not_found for unknown identities so callers can distinguish "we have no record" from "we have a record showing zero activity". Useful for: marketplaces routing work to high-grade agents, peer agents deciding to trust another agent, ops dashboards monitoring an agent's standing, or operators inspecting their own reputation before claiming a wallet.

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• onyx_market_rank

  onyx-mcp

  Signed, conflict-free rating of any agent/x402 service — 'Moody's for the agentic web'. Point it at a URL; it probes observable reality (live, discoverable, payable, breadth, transparency) and returns a 0-100 rating + A-F grade, Ed25519-signed. Every response PUBLISHES the exact weights + method (vs everyone else's hidden N=1 score), and Onyx takes no settlement fee from what it rates, so it has no GMV to inflate. Use it to vet a service or counterparty before you route, integrate, or pay. (price: $0.05 USDC, tier: metered)

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• rug_forecast

  rugcheck-ai

  Heuristic rug-pull forecast — probability and urgency from observable on-chain signals. NOT an ML prediction or guarantee. Weights real factors: active mint/freeze authority, dangerous Token-2022 extensions, no sell route (honeypot), high holder concentration, sells outpacing buys, and very fresh age. Returns a 0-100 probability, an urgency window, and the contributing factors.

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