A Model Context Protocol server for research assistance with ChromaDB vector storage
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# Research Assistant MCP Server A Model Context Protocol (MCP) server that provides research assistance capabilities with ChromaDB vector storage. This server enables AI assistants to save, retrieve, and manage research content efficiently using vector embeddings. ## Features - **Vector Storage**: Uses ChromaDB for efficient storage and retrieval - **Topic Organization**: Organize research content by topics - **Deduplication**: Automatic content deduplication using hashing - **Semantic Search**: Query research content using natural language - **Multiple Topics**: Manage multiple research topics simultaneously - **OpenAI Embeddings**: Uses OpenAI's text-embedding-3-small model ## Installation ### Using uvx (Recommended) ```bash uvx research-assistant-mcp ``` ### Using uv ```bash uv pip install research-assistant-mcp ``` ### Using pip ```bash pip install research-assistant-mcp ``` ### From Source ```bash git clone https://github.com/laxmimerit/research-assistant-mcp.git cd research-assistant-mcp uv pip install -e . ``` ## Configuration ### Environment Variables Required: - `OPENAI_API_KEY` - Your OpenAI API key for embeddings - `RESEARCH_DB_PATH` - Base path for storing research databases - A `research_chroma_dbs` directory will be created inside this path - Example: `/path/to/data` (will create `/path/to/data/research_chroma_dbs`) - Example: `~/.research_assistant_mcp` (will create `~/.research_assistant_mcp/research_chroma_dbs`) Create a `.env` file with your configuration: ```bash OPENAI_API_KEY=your-api-key-here RESEARCH_DB_PATH=/path/to/data ``` ### Claude Desktop Configuration **MacOS**: `~/Library/Application Support/Claude/claude_desktop_config.json` **Windows**: `%APPDATA%\Claude\claude_desktop_config.json` ```json { "mcpServers": { "research-assistant": { "command": "uvx", "args": ["research-assistant-mcp"], "env": { "OPENAI_API_KEY": "your-api-key-here", "RESEARCH_DB_PATH": "/path/to/data" …
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