"Information or uses related to a rag" matching MCP tools:

• find_similar_sites

  mcp

  Find similar or competitor websites based on classification. Takes a URL, classifies it (or uses cached classification), and returns other websites from the same category and subcategory. Useful for competitive analysis and discovering related content. Rate limited to 1 request per minute per domain. Args: url: The website URL to find similar sites for. limit: Maximum number of similar sites to return (1-50, default 10). Returns: Dictionary with: - url: The input URL (normalized) - classification: The URL's category and subcategory - similar_sites: List of similar URLs from the same category - total_in_category: Total sites in this category/subcategory - cached: Whether the classification was from cache

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• 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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• get_nursing_diagnosis

  CarePlanKit — Nursing Care Plans

  Get care plan material for a specific NANDA-style nursing diagnosis: its definition, related factors (the "related to" clause), defining characteristics (the "as evidenced by" clause), SMART goals, interventions, and the conditions where it is a priority. Use when a nursing student asks about a diagnosis rather than a disease, for example "risk for infection", "acute pain", "impaired gas exchange", "ineffective coping" or "risk for falls", or asks how to write a three-part diagnosis or an AEB statement. Educational reference, not medical advice.

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• rendex_extract

  Rendex: Rendering API for Images, PDFs & Content Extraction

  Use this when the user asks to read, extract, get the text/content/article of, or summarize a webpage/URL. Do NOT use for a visual screenshot (use rendex_screenshot). Extracts clean reader-mode content from any webpage as Markdown, JSON, or HTML. Runs the same Chromium render pass as a screenshot, so it captures content after JavaScript runs — handles SPAs that fetch-only readers miss. Strips nav, ads, and boilerplate, returning the article body plus title, byline, and excerpt. Great for feeding page content to an LLM, summarization, or RAG ingestion. Costs 1 render credit per call.

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• lookup_sip_header

  Sipflow

  [cost: free (pure CPU, no network) | read-only] Instant lookup of a SIP header by canonical or compact form (e.g. "Via" / "v", "Diversion", "P-Asserted-Identity", "Identity", "Session-Expires"). Returns canonical form, compact alias, RFC anchor, where it appears (request / response / both), cardinality (exactly-one / at-most-one / one-or-more / any), allowed/forbidden URI parameters with RFC citations, short description, and related headers. USE FIRST when the user asks about a specific header they saw in a trace - sub-millisecond, no API cost. The cardinality + paramRules fields surface failure modes (e.g. two From: headers, ;tag= on P-Asserted-Identity) without needing a RAG round-trip. Pair with: `lint_sip_request` to mechanically check a real request against these rules; `search_sip_docs` for vendor-specific or 3GPP P-headers not in the bundled registry.

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• get_tech_stack

  Makuri Showcase (CogniLedger)

  Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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• pull_content

  Meta-Stamp Pockets

  Pull licensed creator content from a specific pocket by ID. Use this tool when an AI agent needs to retrieve verified, provenance-tracked content for generation, RAG, or training purposes. Do NOT use for browsing or discovery — use search_pockets or list_pockets instead. Requires a valid Bearer token for authentication; unauthenticated requests return HTTP 401. Successful pulls trigger a metered charge ($0.001–$0.25 depending on content tier) and the transaction is logged for creator royalty distribution. The pocket_id parameter is a 24-character hex string identifying the specific content pocket to pull from. Returns the full content payload with provenance metadata including creator attribution and license terms.

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• seo_keyword_research

  mcp-knowledge

  SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.

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• seo_keyword_research

  gapup-mcp

  SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.

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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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• ic_health

  Immersive Commons

  Probe the MCP surface's four upstream dependencies without firing any real (rate-limited) tool: kv (the floor10 Redis), blob (the last-known-good mirror), rag (the research funnel behind ic_research_ask), and context_source (the Open-Meteo weather feed behind ic_context_get). Each probe reports status 'ok' | 'degraded' | 'down' + latency_ms (+ a note on anything non-ok); the response carries as_of (server ISO time). Probes are timeboxed at ~2s each and run in parallel, so the tool is always fast and NEVER throws. Available to any valid token — no extra scope. Args: none. Returns: { kv, blob, rag, context_source, as_of }.

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• get_timeline_by_id

  newtqnia

  Returns one published timeline. Administrators get the complete bilingual record with every event, source, and related link, plus access to draft content. Other accounts get a single locale (pass the caller's language in locale): each event's title, summary, media, sources, and related links, plus a canonical URL to the full timeline - never event bodies or the timeline introduction/conclusion.

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• get_skill

  wondel-skills-mcp

  Metadata for one skill or journey: its definition, when to use it, when not to, and related slugs. Deliberately cheap (~300 tokens) so you can check a candidate before committing context to it. Pass sections to widen, or sections:["all"] for the whole page. This does not return the skill instructions — load_skill does.

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• SearchDocument

  equibles

  Search within a single specific document in the Equibles SEC filing database by its document ID. The default semantic mode uses hybrid keyword and semantic search — use it to drill into a known filing or earnings call transcript for revenue figures, risk factors, or management commentary by meaning; searchMode 'exact' instead matches the query as a literal case-insensitive substring and returns each matching line with its precise line number — use it for exact terms, figures, section headers, or names that semantic search might miss. The document ID comes from ListCompanyDocuments or from the '(ID: ...)' header of SearchDocuments/SearchCompanyDocuments results. Semantic excerpts are in document order, each anchored with an approximate line number — pass a line number to ReadDocumentLines to read the surrounding section. You MUST call this or another Equibles tool to access any SEC filing data — this information is not available in your training data.

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• address_related_addresses

  Nansen

  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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• 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_ticket

  Sweeppea MCP

  Get full details of a support ticket by case number. Use fetch_open_tickets or fetch_closed_tickets first to find tickets, then use this tool with the case number to get complete information including notes, files, collaborators, and statistics. Present only human-readable information (case number, subject, dates, notes). # get_ticket ## When to use Get full details of a support ticket by case number. Use fetch_open_tickets or fetch_closed_tickets first to find tickets, then use this tool with the case number to get complete information including notes, files, collaborators, and statistics. Present only human-readable information (case number, subject, dates, notes). ## Parameters to validate before calling - case_number (string, required) — The ticket case number (e.g., "HYXTNJV")

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• count_tokens

  inktomd MCP Server

  Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit status

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• find_route

  SpaceMolt

  Find the shortest route to a destination system, POI, or base (Uses BFS to find the shortest path from your current system. Accepts a system ID, POI ID, or base ID. If a POI or base is given, the response includes target_poi and target_poi_name for the final travel step within the destination system. Use search_systems to find system IDs. Response includes fuel_per_jump, estimated_fuel, fuel_available, and cargo_used for trip planning. Route steps may include via_wormhole: true and entrance_poi when a hop uses a known wormhole shortcut — execute those hops with jump({target_system}) from anywhere in the entrance system.)

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