
Local FAISS MCP Server
Gives Claude a local FAISS vector database for RAG workflows without external dependencies. Exposes two core tools: ingest_document for chunking and embedding files (PDF, TXT, MD natively, plus DOCX and HTML with pandoc), and query_rag_store for semantic search that returns ranked document chunks. Includes built in prompts for answer extraction with citations and document summarization. Supports custom Hugging Face embedding models and optional two stage reranking with cross encoders for better relevance. Index and metadata persist to disk between sessions. Best when you need document search capabilities in your Claude workflow but want to avoid hosted vector database services or API costs.
- GitHub stars
- 30
- tools
- 2
- transport
- STDIO
- last updated
- Dec 7, 2025
Tools
2 toolsPublic tool metadata for what this MCP can expose to an agent.
ingest_documentIngest a document into the vector store.
Ingest a document into the vector store.
No parameter schema in public metadata yet.
query_rag_storeQuery the vector store for relevant document chunks.
Query the vector store for relevant document chunks.
No parameter schema in public metadata yet.
Sponsors
Readme
Local FAISS MCP Server
A Model Context Protocol (MCP) server that provides local vector database functionality using FAISS for Retrieval-Augmented Generation (RAG) applications.
demo
Features
Core Capabilities
- Local Vector Storage: Uses FAISS for efficient similarity search without external dependencies
- Document Ingestion: Automatically chunks and embeds documents for storage
- Semantic Search: Query documents using natural language with sentence embeddings
- Persistent Storage: Indexes and metadata are saved to disk
- MCP Compatible: Works with any MCP-compatible AI agent or client
Details
- Repository
- github.com/nonatofabio/local_faiss_mcp
- Categories
- Databases, AI & LLM Tools, Documents & Knowledge, Search & Web Crawling
- Package
local-faiss-mcp- Transport
- STDIO
- Registry
- MCP registry, active