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133,382 tools. Last updated 2026-05-25 16:47

"A service for finding the ski resort with the most snow next weekend" matching MCP tools:

  • Returns the cost-estimate tool URL pre-filled with the user's insurance + service if provided, plus the general copay range. The tool URL is a hand-off — the user verifies their plan there for an exact copay.
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  • Get the live operational status of every major AI service tracked by TensorFeed (Claude, ChatGPT, Gemini, Perplexity, Cohere, Mistral, HuggingFace, Replicate, Midjourney, etc). Polled every 2 min. Returns operational | degraded | down per service plus the most recent incident.
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  • Return the calling agent's passport with current reputation tier and receipt count. Recalculates receipt count on every call and auto-upgrades the tier when new thresholds are met (basic 10+, established 100+, trusted 500+, elite 2000+). Includes a hint for the next tier upgrade.
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  • Fetch the full results of a completed Disco run. Returns discovered patterns (with conditions, p-values, novelty scores, citations), feature importance scores, a summary with key insights, column statistics, and suggestions for what to explore next. The response includes a `dashboard_urls` object with direct links to each page of the interactive report — use these to direct the user to the most relevant view: - **summary**: AI-generated overview with key insights, novel findings, and plain-language explanation of the most important findings - **patterns**: Full list of discovered patterns with conditions, effect sizes, p-values, novelty scores, citations, and interactive visualisations - **features**: Feature importances, feature statistics and distribution plots, and correlation matrix - **territory**: Interactive 3D map showing how patterns select different regions of the data Only call this after discovery_status returns "completed". Args: run_id: The run ID returned by discovery_analyze. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.
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  • Hand off an in-flight task to a human operator with a full context bundle: transcript, prior actions, identifiers, and a recommended next step. EXAMPLE USER QUERIES THAT MATCH THIS TOOL: user: "I'm stuck — get a human at smb_xyz to call me back" -> call escalate_to_human({"smb_id": "smb_xyz", "reason": "agent_blocked", "summary": "Cannot resolve via automated channels"}) WHEN TO USE: Use when automated resolution has failed after channel-fallback exhaustion, when the task requires human judgment, or when the customer has explicitly requested human contact. WHEN NOT TO USE: Do not use as a first resort. Escalate only after automated resolution attempts. COST: $0.2 per_escalation LATENCY: ~2000ms EXECUTION: async_by_default (use get_outcome to retrieve result)
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Matching MCP Servers

Matching MCP Connectors

  • The Graph MCP — indexed blockchain data via subgraph GraphQL queries

  • the-committee MCP — wraps StupidAPIs (requires X-API-Key)

  • Vote for a planned service to be built next. Returns JSON: { success, slug, newVoteCount }. 1 sat per vote — multiple votes allowed. Call list_planned_services first to discover valid slugs and current vote counts. Highest-voted services get prioritized. Requires create_payment with toolName='vote_on_service'.
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  • Search the Companies House register by company name or keyword. Returns a paginated list of matching companies with name, number, status, SIC codes, incorporation date, and registered address. Use company_profile for the full record once you have the company number. Re-call with start_index=start_index+items_per_page to fetch the next page.
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  • Predict the next-vintage 128-D Tessera embedding at a cell using a small learned dynamics MLP. Reads the K=3 most-recent attested `geotessera.YYYY` vintages, runs them through an ONNX dynamics head (~200k params, CPU-fast), returns the predicted next-year embedding. The receipt's `model` block carries `model_id`, `version`, `blake2b_hex` (model_cid), training/validation provenance, and `honesty_warnings` flagging `untrained_baseline` when the artifact is the zero-init sentinel. Distinct from v1 (`emem_jepa_predict`) — v1 returns an NDVI scalar via closed-form coefficients; v2 returns a 128-D embedding from a learned model. When to use: Use when you want a forecast in EMBEDDING space rather than NDVI scalar — e.g. to find next-year analogs via `emem_find_similar` against the prediction, or to feed any algorithm in `algorithms_for_topic.foundation_embedding`. Returns 422 with a `/v1/backfill` hint when the cell has fewer than 3 consecutive Tessera vintages cached. Always read the receipt's `model.honesty_warnings` array — when it contains `untrained_baseline`, the prediction is the trivial 'predict last vintage' baseline (treat as no-op).
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  • Search the user's conversation memory. Returns ranked results with content, source timestamps, and confidence scores. For KNOWLEDGE UPDATE questions ('current', 'now', 'most recent'): make two calls — one with scoring_profile='balanced' and one with scoring_profile='recency' — then use the value from the most recent source_timestamp. For COUNTING questions ('how many', 'total'): results may not be exhaustive — search with varied terms and enumerate explicitly before counting. If all results score below 0.3, reformulate with synonyms or specific entity names from the question.
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  • Best first action for a user describing a concern. Runs a parallel lookup across crisis screening, provider availability, and the article corpus, then returns the recommended path (crisis | evaluation | self-help | mixed) with concrete next steps. Optimized for the agent's first turn — a single call replaces 2-3 sequential lookups.
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  • Get an exact sat cost quote for a service BEFORE creating a payment. Useful for budget-aware agents to price-check before committing. No payment required, no side effects. Pass service=text-to-speech&chars=1500, service=translate&chars=800, service=transcribe-audio&minutes=5, etc. Returns { amount_sats, breakdown, currency }. Omit params to see the full catalog of supported services.
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  • Lease a real mobile phone number for receiving SMS OTP codes. Returns the phone number (e164 format) and a session_id needed for all subsequent calls. The number is exclusively yours for ttl_seconds. Next step: use the returned `number` on your target service to trigger an SMS, then call `wait_for_otp` with the returned `session_id`.
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  • Search the Companies House register by company name or keyword. Returns a paginated list of matching companies with name, number, status, SIC codes, incorporation date, and registered address. Use company_profile for the full record once you have the company number. Re-call with start_index=start_index+items_per_page to fetch the next page.
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  • Log a request for a service type not covered by the 10 named tools (e.g. carpet cleaning, dog walking, painting, moving). Does NOT book — adds to the waitlist to signal demand for future service expansion. Use this when none of the book_* tools match the user's need.
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  • Top Hyperliquid perps ranked by absolute funding rate, with OI and annualized yield. Useful for finding the most overcrowded longs/shorts and carry opportunities.
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  • Search the Tonzar B2B catalog of 160,000+ Russian industrial, medical, and agricultural products. Returns matching products with prices, suppliers, and specs. Use for finding Russian equipment for export.
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  • List past orders with optional filters for status, service, country, and a lookback window in days. Returns up to 50 orders (server cap) ordered most-recent-first.
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  • Get the workspace's default device configuration. Returns ``{config, patch}``: ``config`` is the base default config with the workspace's ``config_patch`` applied (the template used when provisioning new devices, including default service versions), and ``patch`` is the raw workspace-level patch object. Useful for inspecting which service versions a fresh device would be created with, or for seeding a payload to ``devices_update_config``. Requires the ``device:read`` scope. Device-scoped api_keys are rejected with 403 — this endpoint is workspace-wide.
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