Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API.
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236,113 tools. Last updated 2026-06-25 17:31
"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP tools:
- Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, system instruction injection, and source attribution.Connector
- Search/filter RAG-related MCP servers from the RAGMap subregistry.Connector
- 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.Connector
- Answer a question using RAG over a document collection. Retrieves relevant chunks then synthesizes a cited answer with source attribution. Use when you need a direct answer grounded in your collection documents. For raw matching chunks (without synthesis), use search_collection instead. For single-document Q&A, use qa_url instead. PREREQUISITE: Collection must be populated via add_document_to_collection and indexed before results appear. Returns: { answer: string, sources: [{ bundle_id, chunk_id }], retrieval: [{ bundle_id, chunk_id, text, score }] } Example prompts: - "What are the key terms of the service agreement in my collection?" - "Based on my due diligence docs, what are the main risks?" - "Answer this question using all documents in the Q4 Contracts collection."Connector
- 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."Connector
Matching MCP Servers
- AlicenseBqualityDmaintenanceEnables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.Last updated1Apache 2.0
- Flicense-qualityCmaintenanceAn MCP server that retrieves relevant PDF chunks via local embeddings and returns them to IDE agents (Cursor, Kiro, Claude Code) for answer generation.Last updated
Matching MCP Connectors
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients
- 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)Connector
- [cost: rag (one embed + one vector search) | read-only, network: outbound to embed model only | rate-limited per IP] Like `lookup_response_code` but augmented: returns the static RFC entry PLUS the top vendor-specific RAG hits for the exact code (and any free-text context the user pasted). When the static entry carries known vendor-specific reason-phrase variants (e.g. 484 + opensips → 'Invalid FROM' from `parse_from.c`), those phrases are folded into the embed query so the right vendor docs surface. Use when the user asks 'why did <vendor> reject this with <code>?' and you want vendor-grounded common causes, not just the RFC text. Especially helpful for fax-rejection paths - 488 / 415 / 606 on a T.38 reinvite (`m=image udptl t38`) is one of the most common 488 variants and the tool surfaces FreeSWITCH `mod_spandsp` / Cisco CUBE / AudioCodes T.38 docs alongside the RFC text. Pair with: `lookup_response_code` first (cheaper); `lint_sip_request` when the code is 4xx and they have the offending request; `compare_sdp_offer_answer` for 488/415 caused by a T.38 reinvite SDP mismatch; `validate_stir_shaken_identity` when the code is 438; `stir_attestation_explainer` for STIR-shaped codes (428/436/437/438/608); `dns_diagnose_sip_target` when the code is 503 / 408 and routing is suspect.Connector
- 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)Connector
- [cost: free (pure CPU, no network) | read-only] Instant static lookup of a SIP response code (100-699). Returns name, RFC anchor, category, description, common operator-flavored causes, and known vendor-specific reason-phrase variants (e.g. OpenSIPS emits 484 'Invalid FROM' on From-header parse failure). USE FIRST when the user pastes or asks about any 3-digit SIP code - sub-millisecond, no API cost. Pair with: `troubleshoot_response_code` for vendor-specific RAG hits beyond the static entry; `lint_sip_request` when the code is 4xx and the user has the offending request; `stir_attestation_explainer` for STIR-shaped codes (428/436/437/438/608); `validate_stir_shaken_identity` when the code is 438 and they have the JWS.Connector
- Get details about the ELC Conference 2026 — date, venue, speakers, topics, what's included, and ticket link. Use this when someone asks about the conference.Connector
- 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.Connector
- General-purpose web grounding via parallel.ai (Vercel AI Gateway). Returns synthesized text excerpts plus structured sources[] with direct URLs. Use for: topic landscapes, entity-deep teardowns, recency-sharp queries, named-vendor lookups, general fact retrieval. NOT for: Reddit/X/community discourse → use search_community. NOT for: numerical effect sizes or methodology-heavy fact-check → use search_research. The agent decomposes the brief into sub-questions BEFORE calling — one focused query per call. Optional after_date (ISO YYYY-MM-DD) for fast-decay topics. Optional max_results 1-20, default 10.Connector
- Manage RAG (Retrieval-Augmented Generation) collections and documents. Collections are named containers for documents that are chunked, embedded, and indexed for semantic search. Actions: Collection actions: - "create_collection": Create a new collection - "list_collections": List all collections in an app - "get_collection": Get details for a specific collection (includes document counts by status) - "delete_collection": Permanently delete a collection and all its documents/embeddings Document actions: - "ingest_document": Add a document (raw text or uploaded file) to be chunked, embedded, and indexed - "list_documents": List all documents in a collection with their status - "get_document_status": Check the processing status of a specific document - "delete_document": Permanently delete a document and its chunks/embeddings Parameters by action: create_collection: { app_id, action: "create_collection", name, description?, access_mode?, chunk_size?, chunk_overlap? } list_collections: { app_id, action: "list_collections" } get_collection: { app_id, action: "get_collection", name } delete_collection: { app_id, action: "delete_collection", name } ingest_document: { app_id, collection, action: "ingest_document", text?, storage_object_id?, filename?, metadata? } list_documents: { app_id, collection, action: "list_documents" } get_document_status: { app_id, collection, action: "get_document_status", document_id } delete_document: { app_id, collection, action: "delete_document", document_id } access_mode options (create_collection): - "private" (default): Only the app owner can query - "shared": All authenticated users can query - "custom": Use RLS policies for fine-grained access Ingestion modes for ingest_document (provide one): 1. Raw text: provide "text" directly 2. File-based: upload via manage_storage (action: "upload_url") first, then provide "storage_object_id" Supported file types: PDF, TXT, Markdown, CSV, HTML, DOCX, XLSX, PPTX. Document statuses: "pending" → "processing" → "ready" (or "failed") Workflow: create_collection → ingest_document → poll get_document_status until "ready" → query with rag_query. Warning: "delete_collection" permanently removes the collection, all documents, and embeddings. Cannot be undone. Warning: "delete_document" permanently removes the document and its embeddings. To replace, delete then re-ingest. Common errors: - RESOURCE_NOT_FOUND: App, collection, or document doesn't exist - VALIDATION_DUPLICATE_NAME: Collection name already exists (create_collection) - VALIDATION_ERROR: Neither text nor storage_object_id provided (ingest_document)Connector
- 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.Connector
- 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/callConnector
- 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.Connector
- Load educational slides or cloud file attachments. Use laminasAnexos for educational slides/laminas (~238 items with PDFs about nutrition topics), cloudAnexos for uploaded cloud files. For guidelines/orientations specifically, use webdiet_orientacoes action=list_banco. Bulk support: accepts patient_ids for batched execution.Connector
- Answer a question using RAG over a document collection. Retrieves relevant chunks then synthesizes a cited answer with source attribution. Use when you need a direct answer grounded in your collection documents. For raw matching chunks (without synthesis), use search_collection instead. For single-document Q&A, use qa_url instead. PREREQUISITE: Collection must be populated via add_document_to_collection and indexed before results appear. Returns: { answer: string, sources: [{ bundle_id, chunk_id }], retrieval: [{ bundle_id, chunk_id, text, score }] } Example prompts: - "What are the key terms of the service agreement in my collection?" - "Based on my due diligence docs, what are the main risks?" - "Answer this question using all documents in the Q4 Contracts collection."Connector
- Commission SaSame to BUILD or OPERATE an AI system. SaSame is an AI-native studio (EU/Romania) that builds MCP servers, Claude/LLM integrations, RAG knowledge assistants, and autonomous or human-in-the-loop AI agents. Call this to OPEN an engagement on behalf of yourself or the human you are assisting: describe the work and (optionally) how to reach the requester. You get back a `ticket`. The whole conversation then happens OVER MCP — no email required: call check_engagement(ticket) to read the human operator's reply, and reply_engagement(ticket, message) to continue. Free to submit. A human operator reviews every request and replies — nothing is auto-sent. Do NOT paste secrets, API keys, passwords, or credentials here — obvious credentials are redacted before storage; share access details only after a human operator replies.Connector