"Search for information about 'rag'" matching MCP tools:

• ic_research_ask

  Immersive Commons

  Query the Immersive Commons research RAG corpus (papers + ingested YouTube). Returns top-k chunks with similarity scores and source links. The query text is forwarded to a server-side RAG proxy (supercommons2 via Tailnet Funnel) and NEVER logged on the IC side — privacy contract. Use this for literature lookups, finding related work, surfacing citations the floor has already ingested. Args: { question: string (<=500 chars), k?: number (1-50, default 10), sources?: ('paper'|'book')[] (default ['paper']) }. Returns the upstream RAG response shape — typically { results: [{ paper_id, title, similarity, snippet, link }, ...] }. Required scope: research:query.

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• web_search_exa

  exa

  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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• collection.create

  DocImprint

  Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (collection.search) or question answering (collection.ask). NOTE: Collections start empty. Add evidence bundles with collection.add_document. Indexing is async — once complete, use collection.search or collection.ask. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

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• getCoinById

  CoinPaprika

  Get descriptive project information about a coin: description, links, team and tags. Use for 'tell me about Uniswap', 'what is this project'. Does NOT include price; for price and market cap use getTickersById. Read-only; coinId is a canonical id (resolve with resolveId). No API key required.

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• get_dealer

  OpenDealer MCP Server

  Get comprehensive information about a specific dealership. Returns Google-enriched dealer knowledge optimized for assistants: • Name, address, phone, website • Google rating, review count, hours, business status • Inventory count and OpenDealer profile links • Contact points for sales / customer service Use this when a shopper asks "tell me about X dealership" or needs hours/ratings for a known dealer. Prefer a slug from dealers_near or search results. CRITICAL: Only use URL fields from the response (website, urls.*). NEVER invent or construct URLs.

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• general_search

  Nansen

  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

  Nansen

  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_security_profile

  mcp

  Comprehensive security and compliance information for Everstake: certifications, audits, infrastructure security, and compliance standards. Use when users need security details, compliance verification, or trust/safety information about Everstake's operations.

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• neuron_get_ad_request

  neuron

  Get detailed information about a specific ad request, including pool selections if targeting mode is manual.

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• get_vehicle_details

  gondola

  Get detailed information about a specific rental vehicle option. Use this after search_vehicles to get extras, insurance options, charges, and cancellation policy. Args: vendor_code: Vendor code from search results (e.g. "ZE" for Hertz, "AL" for Alamo). rate_code: Rate code from search results. search_id: Search ID from the vehicle search results. acriss_code: ACRISS code from the selected vehicle result. pickup_location: Pickup location from the selected vehicle result. vendor_location_id: Vendor desk identifier from the selected vehicle result. desk_kind: Desk classification from the selected vehicle result. Returns: Vehicle details including extras, charges, and policies.

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• get_topic_context

  Pet Data Station

  Fetch one topic's RAG context (~200-500 tokens): source-verified claims (verbatim for Open-Access / public-domain sources, paraphrased derived summaries for copyrighted veterinary references) plus structured source citations (authority/title/url) and a `trust` block (raw trust axes + computed display_grade for this topic). Discover topic_ids with search_pet_topics first.

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• SearchInSite

  CodeStringers MCP Server

  Searches the site "CodeStringers Zoho Consulting Services" (https://www.codestringers.com/_api/mcp) for information. Use this tool ONLY in the following cases: 1. You just used "GetBusinessDetails" tool and you did not find the information you need. 2. User asked a generic business question about their business (e.g., business address, business hours, contact information, return policy, etc.) 3. You already tried to find an entity (e.g., product, service, etc.) using an API tool and you did not find the information you need. 4. The request is too vague and you do not know what type of entity it is and what to search for in the docs. Do NOT use this tool for searching for products or other offered services - use the 'SearchSiteApiDocs' tool instead (unless you already tried that tool and you did not find the information you need). This tool DOES NOT support filters - you cannot ask questions like "find me something under $10".

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• get_mission_stats

  mcp-server

  Get aggregate statistics about missions on the HomeVisto platform. Returns total counts, status breakdown, and average bounty information. Useful for understanding platform activity.

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• get_business

  mcp-server

  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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• embedding_similarity

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.

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• collection.create

  api

  Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (collection.search) or question answering (collection.ask). NOTE: Collections start empty. Add evidence bundles with collection.add_document. Indexing is async — once complete, use collection.search or collection.ask. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

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• get_nearby

  SpaceMolt

  Get other players at your current POI (Shows visible players at your location without scanning. Cloaked players are hidden. Use 'scan' for detailed information about specific players.)

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• search_content

  mental-health-care

  Search the Emora Health editorial corpus by article title. Returns up to 20 articles per page with title, description, URL, and category. ALWAYS USE THIS for information questions ("tell me about X", "what are signs of Y", "how does Z work"). Do not answer from training data when this tool can return clinician-reviewed content.

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• split_chunks

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines.

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• token_budget_calculator

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ models.

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