MyDocsMCP
MCP server that enables semantic search over local PDF collections using local RAG, with automatic indexing of new documents.
README
MyDocsMCP: MCP Server for PDF Collections
This project is a Model Context Protocol (MCP) Server that enables semantic search (local RAG) over a collection of PDF documents. It uses the FastMCP framework, the ChromaDB vector database, and local embedding models from Sentence Transformers.
Architecture
- Semantic Search: 100% local (offline) RAG (Retrieval-Augmented Generation).
- Embeddings:
paraphrase-multilingual-mpnet-base-v2(supports Portuguese). - Vector DB: Persistent ChromaDB.
- Watcher: Monitors new PDFs in the
./data/pdfsfolder and indexes them automatically viawatchdog.
How to Use
1. Data Preparation
Place your PDFs in the ./data/pdfs/ folder. If you want to organize them by disciplines, create subfolders:
data/pdfs/
├── Generative-AI/
│ └── lecture1.pdf
└── Machine-Learning/
└── fundamentals.pdf
The subfolder name will be used as the discipline metadata.
2. Extremely Simple Configuration (Claude / Gemini Desktop)
To use the server, add the configuration below to your agent's JSON file (claude_desktop_config.json or Gemini's settings.json).
Claude Path (macOS): ~/Library/Application Support/Claude/claude_desktop_config.json
Gemini Path (macOS): ~/.gemini/settings.json
The server automatically resolves all data folders (pdfs, metadata, chroma_db) based on the project root. You only need to provide the absolute path where you cloned the repository:
{
"mcpServers": {
"mydocsmcp": {
"command": "uv",
"args": [
"--directory", "/Absolute/Path/To/Your/MyDocsMCP",
"run",
"mydocs-mcp"
]
}
}
}
That's it! No additional environment variables (PYTHONPATH, PDF_DIR, etc.) are required. The setup "Just Works"™.
Exposed Tools
search_documents(query, top_k=5, discipline=None): Semantic search in the collection.list_documents(discipline=None): Lists indexed PDFs.cross_topic_search(query, disciplines): Cross-topic search across multiple disciplines.get_index_stats(): Vector database statistics.ingest_new_documents(path=None, force_reindex=False): Forces manual re-ingestion.
Local Development (Python)
We use the uv package manager:
# Install dependencies
uv sync
# Run the server
uv run mydocs-mcp
Running Tests
uv run pytest
Technologies Used
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