"Ram" matching MCP servers:

Enables external agents to retrieve knowledge with source context through MCP tools for querying, listing collections, and obtaining document summaries.

A modular RAG service framework that exposes tools like query_knowledge_hub, list_collections, and get_document_summary via MCP protocol, enabling AI assistants to perform retrieval-augmented generation.

A pluggable, observable modular RAG framework that exposes query knowledge hub, list collections, and get document summary tools via MCP, enabling AI assistants to perform hybrid search and document retrieval with reranking.

A pluggable, observable modular RAG framework exposing tools via MCP protocol for AI assistants like Copilot/Claude to query knowledge bases, list collections, and retrieve document summaries.

Enables AI assistants to perform retrieval-augmented generation through MCP tools for querying knowledge hubs, listing collections, and retrieving document summaries, with pluggable ingestion, hybrid search, and evaluation components.

Enables AI assistants to analyze documents larger than their context window by loading files into RAM and querying them via search, navigation, and Python execution tools. Supports recursive reasoning to process massive datasets in chunks using sub-agents.

A smart proxy that aggregates multiple downstream MCP servers and enables natural language search for tools, allowing clients like Claude Desktop to discover and use tools through semantic queries.

Builds a local-first digital twin of your microservice ecosystem, enabling natural-language questions about project code, architecture, incidents, and more via hybrid search and a knowledge graph.

An agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.

A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

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 hybrid vector and keyword search over Azure AI Search indexes via an MCP tool, supporting query with embedding generation.

Connects a RAG application to open-webui using Model Context Protocol (MCP), enabling server-to-client communication for context retrieval and tool usage in remote environments through Server-Sent Events (SSE).

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

Exposes hybrid retrieval (dense embeddings + BM25 + RRF) and document operations (search, fetch, rerank) as MCP tools, using Qdrant and OpenAI embeddings for local or server mode.

MCP server that integrates with LM Studio to provide a search_notes tool, allowing the chat model to retrieve and answer from a local Obsidian vault.

A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.

A local MCP server that enables LLMs to securely search and read files within designated Google Drive folders. Supports Google Docs, Google Sheets, PDFs, and plain text with strict folder-scoping via service account authentication.

MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.