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AlaeddineMessadiMCP SERVER

Gemini Embedding 2 MCP

One local memory layer across documents, code, PDFs, images, audio and video — search all of it semantically and get back exact file paths with page context.

by AlaeddineMessadi ↗Vector Stores & RAG RetrievalCommunity

LABSCO SUMMARY

Preview before you index, and context after you search.

Two calls set this apart from the usual local-RAG setup. The dry run tells you what a scan would pull in and why it would skip the rest, which is the check you want before pointing anything at a home directory — dangerous root scans are blocked outright. And after a search, fetching the surrounding chunks or pages means a citation can be verified against the real file instead of trusted. The store stays in a local directory, not a hosted vector database.

What it is

A multimodal RAG server that indexes local files with Google's gemini-embedding-2-preview model into a ChromaDB store that never leaves your machine. Unlike text-only local search, one query reaches across modalities; PDFs are rendered page by page as images for the embedding model while extracted text is kept for citations.

What you get

  • Indexing of a local folder into the store, with wildcard ignore patterns — index_directory
  • A dry run that shows what would be indexed, grouped by modality and skip reason, before the scan happens — preview_directory
  • Semantic search with compact filters for scope, path prefix, item type, extension and modality, so a query can be pinned to one folder or one file type — search_my_documents
  • Neighbouring chunk or page context for a result you already have, for citing the original correctly — get_result_context
  • Index maintenance: list the indexed roots, sync to pick up new, updated and deleted files with ghost-vector cleanup, and remove a directory — list_indexed_directories, sync_indexed_directories, remove_directory_from_index
  • A resource exposing live vector-store statistics — gemini://database-stats

Requirements

A free Gemini API key from Google AI Studio, supplied as GEMINI_API_KEY. Python 3.11 or newer and uv, since the documented path is uvx --from git+https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git@v1.2.1 gemini-embedding-2-mcp — pin the release tag for a stable install, omit it to track main. The index lives at ~/.gemini_mcp_db. A Dockerfile is included; mount that directory as a volume so the store survives restarts.

Setup effort

Add the configuration to your client

See installation options