ickyMCP

# ickyMCP RAG MCP Server for Document Search. Built for legal professionals and business users who need to search across large document collections. ## Features - **Semantic Search**: Find relevant content based on meaning, not just keywords - **Document Support**: PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx), Markdown, Text - **4K Token Chunks**: Large chunks preserve context for legal and business documents - **Incremental Indexing**: Only re-index changed files - **Local Embeddings**: Uses nomic-embed-text-v1.5 (no API costs) - **SQLite Storage**: Single portable database file ## Installation ```bash # Clone or copy the project cd ickyMCP # Create virtual environment python -m venv venv source venv/bin/activate # or `venv\Scripts\activate` on Windows # Install dependencies pip install -r requirements.txt # Or install as package pip install -e . ``` ## Configuration ### Environment Variables | Variable | Default | Description | |----------|---------|-------------| | `ICKY_CHUNK_SIZE` | 4000 | Tokens per chunk | | `ICKY_CHUNK_OVERLAP` | 500 | Overlap between chunks | | `ICKY_DB_PATH` | `./icky.db` | Path to SQLite database | | `ICKY_EMBEDDING_MODEL` | `nomic-ai/nomic-embed-text-v1.5` | Embedding model | ### Claude Code Configuration Add to your `claude_desktop_config.json` or MCP settings: ```json { "mcpServers": { "ickyMCP": { "command": "python", "args": ["/path/to/ickyMCP/run.py"], "env": { "ICKY_CHUNK_SIZE": "4000", "ICKY_CHUNK_OVERLAP": "500", "ICKY_DB_PATH": "/path/to/icky.db" } } } } ``` ## Usage ### Tools Available #### `index` Index documents from a file or directory. ``` index(path="/contracts/2024", patterns=["*.pdf", "*.docx"]) ``` #### `search` Semantic search across indexed documents. ``` search(query="indemnification clause", top_k=10, file_types=["pdf"]) ``` #### `similar` Find chunks similar to a given text. ``` similar(chunk_text="The parties agree to...", top_k=5) ``` #### `refresh` Re-index only files that have changed. ``` refresh(path="/contracts") ``` #### `list` List all indexed documents. ``` list(path_filter="/contracts") ``` #### `delete` Remove documents from the index. ``` delete(path="/contracts/old") delete(all=true) # Clear entire index ``` #### `status` Get server status and statistics. ``` status() ``` ## How It Works 1. **Indexing**: Documents are parsed, split into 4K token chunks with 500 token overlap 2. **Embedding**: Each chunk is embedded using nomic-embed-text-v1.5 (768 dimensions) 3. **Storage**: Embeddings stored in SQLite with sqlite-vec for fast vector search 4. **Search**: Query is embedded, compared against all chunks using cosine similarity 5. **Results**: Top-K most similar chunks returned with full text and metadata ## System Requirements - Python 3.10+ - 4GB RAM (2GB for model + headroom) - ~1GB disk space (model + database) ## License MIT