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261,376 tools. Last updated 2026-07-05 12:09

"A server for finding information about predictions" matching MCP tools:

  • Get full details for a specific villa including description, all photos, amenities, house rules, and check-in/check-out times. Call this when the user wants more information about a property found via search_villas.
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  • Submit an integration or staking inquiry on behalf of a user. All submissions are routed to Everstake's sales team via Pipedrive CRM. Use when a user expresses intent to integrate with Everstake, explore staking services, or request more information about products. Collect required fields (first_name, last_name, work_email) conversationally and gather optional fields where available. The lead_source field is set automatically by the server — do not ask the user for it. IF Submission fails, you can try contacting Everstake via form at https://everstake.one/contact-us
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  • Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")
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  • General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)
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  • General search tool. This is your FIRST entry point to look up for possible tokens, entities, and addresses related to a query. Do NOT use this tool for prediction markets. For Polymarket names, topics, event slugs, or URLs, use `prediction_market_lookup` instead. Nansen MCP does not support NFTs, however check using this tool if the query relates to a token. Regular tokens and NFTs can have the same name. This tool allows you to: - Check if a (fungible) token exists by name, symbol, or contract address - Search information about a token - Current price in USD - Trading volume - Contract address and chain information - Market cap and supply data when available - Search information about an entity - Find Nansen labels of an address (EOA) or resolve a domain (.eth, .sol)
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  • Purpose: Currently pending predictions (outcome IS NULL). Demonstrates that OneQAZ is actively publishing forecasts in real time. Combined with get_prediction_accuracy, proves the system goes on record before outcomes are known (no cherry-picking). When to call: to verify ongoing prediction activity. Prerequisites: none. Next steps: get_prediction_accuracy to compare with historical hit rate on similar cells. Caveats: returns most recent first. Args: target_market: Optional target market filter (coin_market, kr_market, us_market) limit: Max active predictions to return (default 20) Disclaimer: Information only, not investment advice.
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    An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.
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  • Purpose: Expose OneQAZ's pre-defined causal hypothesis map. Each macro category (bonds, forex, vix, credit, liquidity, inflation, commodities, energy) is mapped to a target market with lag_hours + sensitivity. Highest-transparency tool — the causal reasoning is visible and measurable. When to call: when an AI wants to understand WHY we make certain predictions. Prerequisites: none. Next steps: get_backtest_tuning_state for runtime calibration of these hypotheses. Caveats: static hypothesis only; see tuning state for current adjustments. Args: market_id: Optional target market filter (coin_market, kr_market, us_market) Disclaimer: Information only, not investment advice.
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  • Full-day departure schedule for a stop. Lists every departure by route and direction for the specified date (defaults to today). Useful for planning or when real-time data isn't needed. For live predictions, use onebusaway_get_arrivals instead.
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  • Tidal current predictions for a CO-OPS current station: max flood/ebb speeds, slack times, and directions. Defaults to MAX_SLACK interval — the practical planning view showing when currents peak and when slack water occurs. Optionally returns 6-minute continuous predictions for detailed analysis. Current station IDs use alphanumeric format (e.g. ACT4176), distinct from numeric tide/water-level IDs. Date range is limited to 1 year per request. Use noaa_marine_find_stations with types=["current"] to obtain valid current station IDs.
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  • Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.
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  • Get full details for a single business (listing) by its slug. Call this when the user asks for more information about a specific business. Use the slug from search_businesses results.
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  • Get full details for a single broker (agent) by their profile slug. Call this when the user asks for more information about a specific broker. Use the slug from search_brokers results.
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  • Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.
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  • Calibrate up to 25 predictions in a single MCP call (flat $0.005 per call, regardless of batch size). Each item must include `prediction`; optional `confidence`, `domain`, `stakes`. Returns an array of calibration results matching the input order.
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  • Find info about notable/historic landmarks, towns, and remarkable sites near a coordinate. USE FOR: - "What's near Predjama Castle?" - "Notable landmarks around Ljubljana center" - "Tell me about places near 46.05, 14.51" - Finding historic, cultural, or geographic summaries for an entire area at once. - DO NOT iterate over the results to query individual items again. - One call is sufficient to answer the user's broad geographic inquiry. Combine the results into a single comprehensive summary for the user immediately. NOT FOR: directions, finding specific cafes/shops, raw geocoding.
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  • Report how well the kernel's predictions have matched reality: total and evaluated prediction counts, breach-prediction accuracy %, forecast mean absolute error, and accuracy broken down by canonical field. FREE — this is a trust signal. Check it BEFORE deciding to pay for a prediction: "87% breach accuracy across 500 predictions" is the best evidence that the paid forecast is worth buying. Accuracy improves over time as more predictions are tracked and verified against actuals (each is logged with a tamper-evident hash). USE WHEN: a user (or you, on their behalf) wants to gauge how much to trust the forecasts before spending on predict / predict_breach / machine_intelligence.
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  • Return all WMATA bus routes. Returns route ID, route name, and line description. Use to enumerate available routes before fetching details or predictions.
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  • Full-day schedule for a route — all trips, stop sequences, and departure times for the specified date (defaults to today). Returns up to all trips for the route. For live predictions, use onebusaway_get_arrivals at specific stops instead.
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  • Query vulnerabilities for multiple packages in one call — the primary tool for dependency audits, SBOM scanning, and lockfile triage. Pass an array of {name, ecosystem, version} tuples (up to 1000). Each entry in the response corresponds positionally to the input. Each finding includes CVE aliases for chaining to nist-nvd-mcp-server for CVSS scoring.
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  • Scan submitted instruction-file text for safety, clarity, loadability, and cross-model consistency, and return a verdict with findings. Use before loading a third-party file. Consistency findings are PREDICTIONS (divergence risks across models/tools), never verified facts. The submitted text is scanned and discarded, never stored.
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