Andrea9293 MCP

What Andrea9293 MCP MCP server does

Andrea9293 MCP MCP server provides a local knowledge base for documents used by AI coding agents. It can create documents directly from title and content, inspect stored metadata and previews, retrieve full documents, and delete documents together with their chunks, database records, and associated files.

The server also handles files placed in its uploads directory. Supported upload formats listed by the project are .txt, .md, and .pdf. Agents can find the uploads path, inspect available files, and process the directory into searchable content.

How it works

Andrea9293 MCP MCP server runs as a FastMCP process over stdio and starts an Express web interface on port 3080 by default. The web interface provides document browsing, uploads, deletion, search, and context exploration. The same operations are available through a REST API under http://127.0.0.1:3080/api/.

Documents are split into parent and child chunks before local embeddings are generated with Transformers.js. Orama stores document data, vectorized child chunks, and parent chunks in separate local databases. search_documents performs semantic search within one document, while search_all_documents combines full-text and vector matching across the collection. get_context_window returns nearby chunks around a selected chunk index so an agent can gather surrounding context.

Setup and configuration

Install the package through an MCP client with npx:

{
  "mcpServers": {
    "documentation": {
      "command": "npx",
      "args": ["-y", "@andrea9293/mcp-documentation-server"]
    }
  }
}

All documented environment variables are optional. MCP_BASE_DIR changes the storage location from its default under the user home directory. MCP_EMBEDDING_MODEL selects the embedding model, and models are downloaded on first use. Changing models requires documents to be added again because embeddings from different models cannot be mixed.

Set START_WEB_UI to false to disable the browser interface. WEB_HOST and WEB_PORT control its bind address and port. Streaming behavior for large files and the embedding cache can also be configured through environment variables.

Tools and capabilities

Andrea9293 MCP MCP server exposes tools for:

• Adding, listing, retrieving, and deleting documents.
• Listing upload files and processing them into chunks and embeddings.
• Returning the uploads directory and web UI URL.
• Searching within one document or across all documents.
• Retrieving neighboring chunk context.
• Running Gemini-powered document search when GEMINI_API_KEY is available.

The optional search_documents_with_ai tool uses Google Gemini rather than only local embeddings. Without that key, the local embedding-based search tools remain available.

Limitations and notes

The server is designed for local-first operation and does not require an external database or cloud API for its standard embedding workflow. Local embedding models may download approximately 80–420 MB on first use. Storage consists of local Orama files, Markdown copies, and the uploads directory under MCP_BASE_DIR.

The default web UI binds to 127.0.0.1:3080; setting WEB_HOST to 0.0.0.0 exposes it on all interfaces. Gemini search depends on a valid GEMINI_API_KEY and is not part of the offline-only path.