"Information on RAG Documents or Processing" matching MCP servers:

RAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.

Enables AI assistants to search NHTSA recall documents and query governed dbt analytics via validated, read-only text-to-SQL, returning citable evidence and data freshness without granting database or file access.

This read-only MCP Server allows you to connect to XML Documents data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for XML Documents (https://www.cdata.com/drivers/xml/download/mcp).

A privacy-preserving local RAG system integrated with MCP, enabling natural language queries over ingested documents and a SQLite database through vector search and local database tools.

Connects to a PostgreSQL database (pgvector) to perform semantic retrieval via the search_documents tool, returning raw document snippets for LLM synthesis.

Enables read-only document retrieval and SQLite exploration through tools over stdio, enforcing a safety gate before SQL execution.

This read-only MCP Server allows you to connect to Act-On data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.

Read-only MCP server that exposes ZAP's RAG search modes (plain, hybrid, hybrid_rerank) for agents to perform semantic code retrieval.

Retrieves comprehensive patient information including personal details, insurance plans, and employee data through the get_patient tool. Supports Docker Compose deployment and integration with Watson Orchestrate for healthcare data access.

Processes emails from Outlook with date filtering, storing them in SQLite databases while generating vector embeddings for semantic search capabilities in MongoDB.

Enables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.

A private MCP server that converts natural-language questions into embeddings and performs semantic retrieval over document chunks stored in BigQuery, returning structured passages with source and page metadata. It serves as the read-only retrieval layer for a document-grounded chatbot.

A Model Context Protocol server for reading TiDB RAG knowledge base, enabling browsing, searching, and vector search of knowledge entries.

Enables RAG (Retrieval-Augmented Generation) with tools for vector search, document access, and OpenAI integration, plus MySQL storage and file system operations via MCP.

Stores Organizations content

A powerful AI service platform that provides complete MCP tool calling capabilities and RAG knowledge base functionality, enabling users to connect to multiple MCP servers and perform intelligent document search.

MCP server platform enabling agents to self-register and access geo MCP tools (read-only geo_entities) plus hello ping services via a public MCP endpoint.

Enables a unified AI assistant that combines document retrieval (RAG), database queries via MCP tools, and web search, allowing users to ask complex questions and receive answers from multiple enterprise sources.

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.