Overview

The MCP Txtai Server enables AI models to interact with Txtai, an AI-powered search engine that builds vector indexes (also known as embeddings) to perform similarity searches. Txtai is widely used for semantic search, retrieval-augmented generation (RAG), and building intelligent applications.

Community Server:

Developed and maintained by rmtech1

Key Features

⚡

Semantic Search

Perform semantic search across stored memories

🔑

Persistent Storage

Persistent storage with file-based backend

⏰

Tag-based Memory

Tag-based memory organization and retrieval

💾

Claude and Cline AI Integration

Integration with Claude and Cline AI

Available Tools

Quick Reference

Tool	Purpose	Category
`store_memory`	Store new memory content	Write
`retrieve_memory`	Retrieve memories based on semantic search	Read
`search_by_tag`	Search memories by tags	Read
`delete_memory`	Delete a specific memory	Write
`get_stats`	Get database statistics	Discovery
`check_health`	Check database and embedding model health	Discovery

Detailed Usage

store_memory▶

Store new memory content with metadata and tags.

use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "store_memory",
  arguments: {
    content: "Important information to remember",
    tags: ["important"]
  }
});

Returns a confirmation message.

retrieve_memory▶

Retrieve memories based on semantic search.

use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "retrieve_memory",
  arguments: {
    query: "what was the important information?",
    n_results": 5
  }
});

Returns a list of matching memories.

search_by_tag▶

Search memories by tags.

use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "search_by_tag",
  arguments: {
    tags: ["important", "context"]
  }
});

Returns a list of matching memories.

delete_memory▶

Delete a specific memory by content hash.

use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "delete_memory",
  arguments: {
    content_hash: "hash_value"
  }
});

Returns a confirmation message.

get_stats▶

Get database statistics.

use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "get_stats",
  arguments: {}
});

Returns database statistics.

check_health▶

Check database and embedding model health.

use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "check_health",
  arguments: {}
});

Returns the health status of the server.

Installation

{
  "mcpServers": {
    "txtai-assistant": {
      "command": "path/to/txtai-assistant-mcp/scripts/start.sh",
      "env": {}
    }
  }
}

Custom Connection:

The server can be configured using environment variables in the .env file.

Common Use Cases

1. Store and retrieve memories

Store and retrieve memories based on semantic search:

// Store a memory
use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "store_memory",
  arguments: {
    content: "Important information to remember",
    tags: ["important"]
  }
});

// Retrieve memories
use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "retrieve_memory",
  arguments: {
    query: "what was the important information?",
    n_results": 5
  }
});

2. Search by tags

Search memories by tags:

use_mcp_tool({
  server_name: "txtai-assistant",
  tool_name: "search_by_tag",
  arguments: {
    tags: ["important", "context"]
  }
});

Sources

Sentry MCP Server

Sentry MCP servers enable AI models to interact with Sentry's error monitoring and performance tracking platform, providing capabilities for analyzing errors, tracking performance, and assisting in debugging applications.

Filesystem MCP Server

Filesystem MCP servers enable AI models to interact with local file systems, providing capabilities for file operations, directory management, and secure file access within specified boundaries.

MySQL MCP Server

MySQL MCP servers enable AI models to interact with MySQL databases, providing capabilities for structured data operations, SQL queries, transaction management, and relational data management.

Txtai MCP Server