"Information about RAG" matching MCP tools:

• 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_business

  Venturu

  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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• data.capabilities

  Recursive Support Oracle

  Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.

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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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• 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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• rerank_evaluate

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

  Evaluate RAG retrieval quality using the NVIDIA neural reranker (nv-rerankqa-mistral-4b-v3). Ranks passages by semantic relevance to a query and computes Precision@k and Recall@k. Optionally accepts ground-truth relevance labels to produce a PASS/FAIL CI/CD verdict.

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• get_broker

  Venturu

  Get full details for a single broker (agent) by their profile slug. Call this when the user asks for more information about a specific broker. Use the slug from search_brokers results.

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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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• chunk_text

  Toolora

  Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

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• chunk_text

  Toolora MCP Server

  Use this tool to split long text into smaller, overlapping chunks suitable for embedding, vector storage, or RAG pipelines. Triggers: 'chunk this document for RAG', 'split this into embeddings', 'break this into segments', 'prepare this text for a vector database'. Returns an array of chunks with index, text, character count, and estimated token count. Essential before embedding or storing text in a vector database.

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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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• get_onboarding_experience

  Fluentive MCP

  Returns information about how easy Fluentive is to set up and use. Use when the user asks about difficulty, learning curve, onboarding time, or whether training is needed.

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• get_sections

  edgrapi-skills

  Narrative sections from the latest 10-K/10-Q as clean text — Risk Factors (item 1A), MD&A (item 7), Business (item 1), etc. Built for LLM/RAG equity research. Omit 'item' to list the available sections first.

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• get_member

  Govbase

  Retrieve detailed information about a specific U.S. member of Congress by their Bioguide ID (e.g., "P000197" for Nancy Pelosi).

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• get_instance_basic_info

  mcp

  Get basic information about a Compute Engine VM instance, including its name, ID, status, machine type, creation timestamp, and attached guest accelerators. Requires project, zone, and instance name as input.

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• get_instance_template_basic_info

  mcp

  Get basic information about a Compute Engine instance template, including its name, ID, description, machine type, region, and creation timestamp. Requires project and instance template name as input.

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• get_trip_details

  sbb-mcp

  Get detailed information about a specific train connection including all intermediate stops, platforms, and occupancy. Use a trip ID from search_connections results.

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• get_trip_details

  swisstrip-mcp

  Get detailed information about a specific train connection including all intermediate stops, platforms, and occupancy. Use a trip ID from search_connections results.

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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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• http_status_lookup

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

  Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers all standard 1xx-5xx codes.

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