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225,631 tools. Last updated 2026-06-22 20:32

"Information about novels or works of fiction" matching MCP tools:

  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.
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  • Get complete product information about Savvly, an SEC-registered investment fund offering longevity protection — use it whenever the user asks what Savvly is, how it works, its fees, eligibility, or payouts, or wants an overview. Pass `section` to focus the answer (default 'all'). It renders an interactive product overview card the user expects to see. These facts come from Savvly's own current records; the response includes primary sources (e.g. SEC filings) for reference.
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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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  • Retrieve the plain-text content of a Project Gutenberg book, stripped of the standard license header and footer so the response contains only the literary work. For long works — novels routinely run 500KB–2MB — use offset and limit to read in chunks rather than fetching the whole book at once. The response reports totalChars and remainingChars so the caller can page through without guessing. Prefers UTF-8 plain text; falls back to ASCII plain text; refuses audio books (media_type "Sound") with a clear error.
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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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  • Get detailed information about a nonprofit organization by EIN. Returns comprehensive data from the organization's IRS 990 filings including revenue, expenses, assets, executive compensation, and filing history. Use search_nonprofits first to find the EIN. Args: ein: Employer Identification Number (e.g. '13-1837418' or '131837418').
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Matching MCP Servers

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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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Matching MCP Connectors

  • Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.
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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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  • Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.
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  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.
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  • Returns the full text of a single Hemrock concept doc by slug. Use this to learn how a financial-modeling calculation actually works before building or auditing it. Get valid slugs from list_concepts.
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  • Get information about related addresses of an input address. Note: This only includes the the "special" connections 'First Funder', 'Signer', 'Previous Signer', 'Multisig Signer of', 'Previous Multisig Signer of', 'Deployed via', 'Deployed by', 'Deployed Contract', 'Created Contract', 'Created by'. To get related wallets, also check address counterparties. First funder exchange withdrawal address does usually NOT belong to the same entity as the address, only deposit addresses. Only information is that it has been funded by the exchange.
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  • Get a concise explanation of what Crinkl is and how the protocol works. Use this first if you have no prior context about Crinkl. Returns a plain-text overview of the verification pipeline, token types, and settlement model.
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  • Walk the citation graph one hop from a seed work. Direction picks the edge: incoming citations (`cites`), the seed's own references (`cited_by`), or OpenAlex's algorithmically-related works (`related_to`). Note: `direction` follows OpenAlex's filter convention, which inverts the common English reading — `cites` returns works that cite the seed; `cited_by` returns works the seed cites. Results use the works schema; combine with filters/sort to narrow further.
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  • Compare the tag profiles of two content entities (franchises or works) and measure how similar they are. Returns a Jaccard similarity score, the list of shared tags, the tags unique to each entity, and a breakdown of shared tags by facet. When to use this tool: an agent needs to compare two franchises or works (e.g. 'how similar are Dark Souls and Elden Ring?', 'what do Street Fighter and Mortal Kombat have in common?', 'on which axes do these two games differ?'), find positioning overlap, identify cross-sell opportunities, or answer 'if you liked X you might like Y' questions backed by data. Works for any domain (video-games, music, film, tv).
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  • Search Open Library authors by name. Returns Open Library Author IDs, names, birth/death dates, top works, and subject associations. Use author IDs for openlibrary_get_author (bio, remote IDs) or openlibrary_get_author_works (list of works).
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  • Browse works by subject. Returns matching works with edition counts and cover IDs, plus the total work count for the subject. Subjects are user-contributed and may be inconsistent ("science fiction", "Science fiction", "SF" are separate tags). Try lowercase forms first.
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  • Get answers to frequently asked questions about Savvly. Use when the user has specific questions about how Savvly works, fees, withdrawals, or regulatory status. For richer, audience-specific Q&As (employee / advisor / broker / employer), use `search_savvly_content` instead. These facts come from Savvly's own current records; the response includes primary sources (e.g. SEC filings) for reference.
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  • Architecture reference for Fractera AI Workspace: what it is made of and how it works (the admin drives it through Hermes — chat Web UI or Telegram — or directly through the five coding agents; a modal subscription sign-in layer + MCP keep work resilient when a subscription is limited; LightRAG is the central memory that slashes token use; Hermes is a light orchestrator while the coding agents do the heavy lifting; the result ships over HTTPS on a custom domain or plain HTTP on an IP). RETURNS A DIAGRAM IMAGE URL you can show the user when they ask what Fractera is or how it works. Call with NO arguments to get the wide illustration URL + the core "how it works" scenario + the section list; call again with a single `section` id to read one entity in depth.
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  • ⚠️ DESTRUCTIVE: Permanently delete an endpoint. Cannot be undone. Only works for agent-created endpoints. PATs are preferred and require mcp:endpoints:write or mcp:*.
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