"RAG MCP servers with quality validation" matching MCP servers:

Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.

Enables AI agents to screen and filter stock universe, compute custom quality scores, inspect score history, manage scoring systems, and generate shareable screen links, acting as the signed-in user.

MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.

A modular RAG server with hybrid search and LLM rerank, exposing 3 MCP tools for AI clients like Claude Desktop and Cursor.

A toolkit for managing Claude desktop app extension configurations, enabling features like Brave search, file operations, Git/GitHub integration, shell access, web scraping, HTTP requests, AWS documentation search, and Obsidian note management.

Enables AI agents to manage virtualization, Active Directory, Windows hosts via WinRM, PostgreSQL databases, and shell/SSH environments through Model Context Protocol tools.

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Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.

MCP server that enables semantic search over PDF documents using RAG with ChromaDB and OpenAI embeddings.

MCP server for Fathom Works RAG that exposes a self-hosted knowledge base as tools for any MCP-capable LLM to query documents, manage libraries, and ingest files or URLs.

Enables any MCP host to search and answer over your own documents with hybrid BM25+dense retrieval, cross-encoder reranking, and grounded, cited responses that refuse when no evidence is found.

Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.

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

Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.

Provides local hybrid search over files by combining semantic vector and full-text BM25 retrieval, with MCP stdio/HTTP support, OAuth 2.1 authentication, and tools for reading and converting documents to Markdown.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.

MCP server that integrates a 1200-paper RAG database with six tools to support research workflows across stages like hypothesis, experiment, statistics, and writing. It routes requests to specialized skills and real-time frontier searches to provide evidence-grounded research mentoring.

Enables local knowledge base management with retrieval-augmented generation (RAG), providing semantic search, document reading, listing, and Q&A via MCP tools and REST endpoints, all running locally without cloud dependencies.

An MCP server that ingests Spring Boot REST microservices from GitHub, builds a request-flow ontology, and enables natural-language queries about code with flow-aware answers and citations.