"Information or Uses for a Rag" matching MCP tools:

• purchase_enterprise_license

  Opedd — Licensed Content for AI

  Purchase a bulk enterprise license covering multiple publishers (Phase 10). Returns a Stripe client_secret for payment completion + the enterprise_license_id. After payment, an ent_* access key is emailed to buyer_email. Scopes: 'custom' (pass-through publisher_ids), 'platform_wide' (auto-resolve all opted-in publishers), 'filtered' (Phase 10 filter_rules). License tiers: 'rag' (= ai_retrieval), 'training' (= ai_training, flat-fee not metered), 'inference' (= ai_retrieval), 'full_ai' (writes both retrieval + training records). The buyer must accept the Opedd Master Services Agreement (opedd.com/terms) before purchase — set terms_accepted=true to record it.

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• firecrawl_parse

  firecrawl-mcp

  Parse one supported document into markdown, HTML, links, summary, targeted answers, or JSON matching a schema. Supported inputs include common HTML, PDF, Word, RTF, OpenDocument, and spreadsheet files; PDF parsing can be bounded with `pdfOptions.maxPages`. Local MCP reads `filePath` from the server filesystem. Hosted MCP uses two calls: first provide `filePath` to receive upload instructions, upload locally, then call again with the returned `uploadRef`; do not send both fields together. Remote web URLs belong in `firecrawl_scrape`. Set `redactPII` to request redaction of personally identifiable information in the returned content. `zeroDataRetention` requires an eligible authenticated account; omit it for anonymous keyless use. Returns upload instructions for hosted phase one or parsed document content for the final call.

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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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• axint.schema.compile

  Axint

  Compile a minimal JSON schema directly to Swift, bypassing the TypeScript DSL entirely. Supports intents, views, components, widgets, and full apps via the 'type' parameter. Uses ~20 input tokens vs hundreds for TypeScript — ideal for LLM agents optimizing token budgets. Use: use for token-light JSON-to-Swift generation; use compile for full TypeScript DSL control and scaffold for TS starters. Inputs: schema kind selects intent, view, widget, or app output; options add companion metadata. Effects: read-only Swift generation; writes no files and uses no network.

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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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• 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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• hallucination_check

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

  Word-overlap based hallucination check: verifies if an LLM answer's words and numbers appear in the provided source/context. Fast, deterministic, no API key needed. Limitations: not semantic — does not understand synonyms or paraphrases. For true semantic grounding, use run_semantic_tests with embedding mode. Essential for quick RAG accuracy testing.

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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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• 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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• factreason_discover_api

  FactGrid (Renamed to FactReason)

  Find third-party APIs for a capability when the provider is unknown. Supply a concise capability such as "sms" or "manage DNS"; returns ranked services, example endpoints, and a next step. Uses metered access. When the provider is already known, call factreason_integration_brief instead.

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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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• 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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• compare_evidence

  Healthy Aging Atlas

  Compare mapped human-evidence strength and limitations for two to five supplements for the same goal. This is an evidence comparison, not a product ranking or purchase recommendation, and contains no affiliate links. Use only for public, non-personal evidence questions. Do not call this tool for requests involving personal or sensitive health information, including medical records, medication lists, diagnoses, symptoms, laboratory results, or treatment planning. Tell the user not to submit that information and direct them to a qualified healthcare professional.

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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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• 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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• 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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• get_funder_profile

  foundation-discovery

  Get detailed profile information for a specific funder. Polymorphic identifier — pass ``ein`` for US 990 foundations OR ``funder_id`` (bare UUID / ``n9f:<uuid>``) for non-990 funders such as European, UK 360Giving, and Canadian CRA T3010 funders. ``search_funders`` returns both fields on every hit, so the caller can hand either one back here. At least one identifier must be supplied. Use this after searching for funders to get detailed information about a specific one.

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