"MCP with RAG capabilities" matching MCP servers:

A Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.

Enables hybrid search (BM25 + semantic with RRF) over documentation, with Thai language support. It exposes MCP tools for adding documents and searching via Qdrant and Ollama/OpenAI, using a stateless Streamable HTTP transport.

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

Provides coding agents with hybrid semantic and exact-identifier search across VHDL source, documentation, and general code, with cross-referencing and exact source attribution.

Enables LLMs to scrape, index, and search modern and legacy Salesforce documentation by piercing complex Shadow DOM and iframe structures. It supports hierarchical spidering of entire guides and local RAG capabilities using a SQLite database for offline querying.

Provides read-only retrieval of STM32 datasheet and reference manual content with exact citations, page numbers, register details, and source hashes so Claude can answer questions or generate code from verified evidence.

Enables question answering over technical documents (Markdown, TXT, PDF) using hybrid retrieval (vector + lexical) with cited sources and audit logging, fully locally with optional cloud fallback.

MCP server providing RAG-powered access to Semarchy documentation (xDM, xDI, xDG) for AI assistants.

Provides read-only RAG retrieval, document access, source auditing, status, and health checks for CUMCM optimization and decision modeling contexts.

Enables LLMs to interact with Outline wiki for document management, search, and collections, with optional AI-powered features including RAG-based Q\&A, semantic search, and document summarization.

Integrates AI assistants with the Unity Editor for scene manipulation, asset management, and testing, plus Unity documentation search via RAG.

A Model Context Protocol server implementation that enables seamless integration with Claude and other MCP-compatible clients to access Prem AI's language models, RAG capabilities, and document management features.

MCP server for semantic and hybrid search over RHEL documentation using docs2db RAG, with cross-encoder reranking and support for multiple MCP clients.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.

Enables hybrid search and agentic retrieval over Confluence pages via MCP tools, allowing Claude Desktop, Cursor, or Claude Code to search and retrieve Confluence content.

An MCP-based service that enables AI models to seamlessly interact with Feishu (Lark) platform, supporting document reading and chatbot messaging capabilities.

MCP server that provides RAG-based access to Neoforge Mod API documentation, enabling LLMs to query the latest Neoforge modding knowledge.

Enables RAG-powered documentation search using OpenAI embeddings and Pinecone vector database. Provides an extensible framework for adding additional tools with support for both local STDIO and production HTTP transports.

Transforms static gemini-cli documentation into a queryable RAG service, enabling developers to ask questions about Gemini CLI in natural language and receive instant, accurate answers based on the official documentation directly within their workflow.

Enables to search and retrieve chunks from a fictional library's documentation through three tools (kb_search, kb_fetch, kb_sources), allowing LLMs to perform RAG queries via the Model Context Protocol.