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

• search_airports

  airports

  Look up an airport by city name (e.g. "Tokyo", "New York", "London") OR by 3-letter IATA code (e.g. "JFK", "LHR"). City lookup uses a bundled map of the top ~150 international hubs; cities with multiple airports return all primary ones. For airports not in the bundle, pass an IATA code or use the aviationstack pack for full-text name/country search.

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• knowledge_query

  dialogbrain

  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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• create_collection

  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 (search_collection) or question answering (ask_collection). NOTE: Collections start empty. Add evidence bundles with add_document_to_collection. Indexing is async — once complete, use search_collection or ask_collection. 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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• 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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• url-encoder

  Microapp

  URL-encode or URL-decode a string. Uses RFC 3986 component encoding (encodeURIComponent semantics).

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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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• 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... Use: use for token-light JSON-to-Swift generation; use compile for full TypeScript DSL control. Effects: read-only Swift generation; writes no files and uses no network.

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• create_booking

  Teres — Booking for AI Agents

  Create a new booking/appointment at a business. Requires customer information (name and email) and a selected time slot. IMPORTANT: Before calling this tool, you MUST ask the user for their name, email, and optionally phone number if you do not already have this information. Do not guess or fabricate customer details. Returns a booking confirmation with a unique booking_id.

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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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• content_similar

  gapup-mcp

  Find content entities similar to a given one. For embedded franchises this uses SEMANTIC vector similarity (pgvector) over the enrichment profile — surfacing entities that feel alike even when their tags differ literally. Falls back to shared enrichment-tag overlap for works or non-embedded entities. Each result carries a similarity score and its entity-level freshness/confidence (verifiable, sourced). When to use this tool: an agent wants recommendations or lookalikes for a franchise or work. Input: an entity_id and its type.

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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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• fix_gherkin

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

  Fix Gherkin syntax warnings from a jira_to_test_suite result. Takes the current gherkin text and the _gherkin_warnings array, calls your LLM to fix ONLY the flagged issues (adds missing Given/When/Then steps, etc.), and returns the corrected Gherkin. Lightweight — uses ~300-500 tokens vs ~5k for a full regeneration. Requires BYOK LLM key.

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• scheduling_book

  Servicialo MCP Server

  Book a session (Servicialo spec). Returns confirmation_credential (opaque token, valid 30 min) and booking_id. Use scheduling_confirm with the credential to finalize. Does NOT require an API key — uses requester identity (fullName + email or phone). Accepts optional submission context for audit trail.

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• set_store_flow_state

  Power Automate MCP Server by Flow Studio

  [Requires Pro+ plan] [DEPRECATED — scheduled for removal] Start or stop a Power Automate flow via the live Power Automate API, then persists the updated state back to the Power Clarity store. Uses impersonation via a cached service account that is either a flow owner or an environment admin. Returns the updated stored flow record. Use set_live_flow_state instead.

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• knowledge_query

  DialogBrain

  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_decision

  Rettsarkiv

  Hent én avgjørelse med stabil id (HR-2024-123-A eller Rt-1979-524). Returnerer strukturert tekst, lov-taggede §-referanser og provenance (source_origin + content_hash). Hver §-tag har lesbar overskrift (section_heading). Sett paragraphs=true for nummererte avsnitt-chunks (pinpoint «avsnitt 45») med arvede §-tags — bruk det for sitérbar RAG-kontekst. Sett statutes=true for å få selve gjeldende lovtekst (section_text) på hver tag.

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• markdown

  mercury-x402-mcp

  URL → clean, LLM-ready markdown (boilerplate/nav/ads stripped, headings + lists + links preserved) with a signed provenance receipt pinning the markdown to its source — the RAG-ingest primitive. Deterministic (no LLM): same URL + same source bytes ⇒ byte-identical markdown. — $0.005/call

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• document-qa-prep

  The Stall

  Prepares a document for question-answering and RAG pipelines. Chunks the input text at paragraph/sentence boundaries, assigns deterministic chunk IDs, estimates token counts, and extracts document metadata (word count, type, headings). Returns ready-to-embed chunks with overlap support. No LLM or external API — pure text processing. Use mid-task when you've fetched a document and need it split before querying a vector store.

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• iliad_vector_database

  AXIS Toolbox — Agentic Commerce Codebase Intelligence

  AXIS-owned vector store. Two operations: `upsert` (insert or replace vectors) and `query` (cosine top-k nearest neighbors). Namespaces are account-scoped server-side (`acct:<account_id>:<namespace>`), so tenants cannot read each other's vectors. Persistent across restarts via Postgres. Requires Authorization: Bearer <api_key>. Best for RAG retrievers, deduplication, and similarity search. Engineer mode (X-Agent-Mode: engineer — Managed Memory, $0.05): query runs a pgvector/HNSW ANN candidate pool with optional recency-decay reranking (recency_half_life_days — managed forgetting), RRF hybrid fusion (sparse_ids), and metadata filter; upsert applies intra-batch semantic-dedup (dedup_threshold).

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• draw_winners

  Sweeppea MCP

  Draw winners from a sweepstakes immediately. Use fetch_sweepstakes first to get the sweepstakes_token, and fetch_groups to get available groups. CRITICAL: This is a production operation that selects real winners. ALWAYS confirm with the user before drawing — including the number of winners and which group to draw from. Uses weighted random selection favoring participants with bonus entries. Cannot draw from paused or archived sweepstakes. Use them internally for tool chaining but present only human-readable information (names, emails). # draw_winners ## When to use Draw winners from a sweepstakes immediately. Use fetch_sweepstakes first to get the sweepstakes_token, and fetch_groups to get available groups. CRITICAL: This is a production operation that selects real winners. ALWAYS confirm with the user before drawing — including the number of winners and which group to draw from. Uses weighted random selection favoring participants with bonus entries. Cannot draw from paused or archived sweepstakes. Use them internally for tool chaining but present only human-readable information (names, emails). ## Pre-calls required 1. fetch_sweepstakes if the user gave you a sweepstakes name instead of a token 2. fetch_rules(sweepstakes_token) — confirm Official Rules exist (drawing is illegal without them) 3. count_participants — verify there are enough entries for the requested winners count 4. Confirm the entry period has ended for the relevant drawing window ## Parameters to validate before calling - sweepstakes_token (string, required) — The sweepstakes token (UUID format) - how_many_winners (number, required) — Number of winners to pick (must be >= 1) - group (string, required) — Group token to draw from, or "allgroups" for all participants - completed_entries (boolean, optional) — Only include participants with completed entries (default: true) - include_opted_out (boolean, optional) — Include participants who opted out (default: false) - exclude_spam (boolean, optional) — Exclude flagged spam participants (default: true) ## Notes - After drawing: fetch_winners to confirm, update the campaign brief note, create a calendar event for winner notification deadline - Remind the user about web-interface steps: classify winners, send notifications, publish Winners List

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