@rmtech1
基本信息
Model Context Protocol (MCP) server implementation for semantic vector search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic vector database search capabilities. You can use Claude and C
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TxtAI Assistant MCP is a Model Context Protocol (MCP) server for semantic search and memory management, built on the open‑source txtai AI‑powered search engine. It provides an API for storing, retrieving, and managing text‑based memories with semantic search capabilities, and is designed for integration with AI assistants such as Claude and Cline.
Clone the repository and run the ./scripts/start.sh script, which creates a virtual environment, installs dependencies, sets up directories, and starts the server. Configure the server via a .env file (template provided). Then add the server configuration to Claude’s or Cline’s MCP settings file to enable the available MCP tools.
It integrates the txtai semantic search engine with the Model Context Protocol, allowing AI assistants like Claude and Cline to directly store and search memories using MCP tools while benefiting from txtai’s neural search, zero‑shot classification, and multi‑language support.
Python 3.8 or higher, pip, and a virtual environment (recommended). The startup script installs all required Python dependencies automatically.
Memories and tag indexes are stored as JSON files in the `data` directory (`memories.json` and `tags.json`). Logs are stored in the `logs` directory.
Yes. The `MAX_MEMORIES` environment variable (default 0, meaning no limit) can be set to cap the number of stored memories.
The server uses HTTP with configurable CORS origins (set via the `CORS_ORIGINS` environment variable). Input validation is performed on all endpoints, and file paths are sanitized to prevent directory traversal.
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