Optimized Memory MCP Server v2

@AgentWong

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

AgentWong

关于 Optimized Memory MCP Server v2

This is a personal project to test Claude AI's ability to self-write an MCP Server code for its own use.

#mcp #server #claude-ai

Optimized Memory MCP Server v2 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "optimized-memory-mcp-serverv2": {
      "command": "uvx",
      "args": [
        "run",
        "python",
        "-m",
        "src.main"
      ]
    }
  }
}

Optimized Memory MCP Server v2 工具

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当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Optimized Memory MCP Server v2?

Optimized Memory MCP Server v2 is a high-performance Python-based Model Context Protocol (MCP) server implementation optimized for Claude Desktop integration. It provides efficient memory management and robust infrastructure component tracking capabilities, using SQLite for persistent storage with connection pooling.

How to use Optimized Memory MCP Server v2?

Clone the repository, set up a Python environment (Python 3.13.1+), install dependencies with pip install -r requirements.txt, configure the database URL via the DATABASE_URL environment variable, run database migrations with alembic upgrade head, then start the server with uvx run python -m src.main. In Claude Desktop, set the MCP server URL to http://localhost:8000 and enable the MCP protocol in settings.

Key features of Optimized Memory MCP Server v2

MCP resources for entity, provider, and Ansible collection management
MCP tools for entity creation, observation, and provider registration
Full-text search across tracked infrastructure components
SQLite database with connection pooling and automatic cleanup
Comprehensive error handling and extensive logging
Full Claude Desktop compatibility via FastMCP

Use cases of Optimized Memory MCP Server v2

Tracking infrastructure components and their relationships in a centralized store
Managing Ansible collections and provider resources through MCP tools
Performing full-text search on entities, observations, and metadata
Integrating with Claude Desktop for AI-assisted infrastructure analysis

常见问题

What are the runtime requirements?

Python 3.13.1 or higher, SQLite 3.x, and the uvx server. Dependencies are listed in `requirements.txt`.

How do I start the server?

Run `uvx run python -m src.main` after installing dependencies, configuring the database URL, and applying migrations with `alembic upgrade head`.

How do I connect Claude Desktop to this server?

Set the MCP server URL in Claude Desktop to `http://localhost:8000` and enable the MCP protocol in Claude settings. Verify the connection with `curl http://localhost:8000/health`.

How is data stored?

Data is stored persistently in a SQLite database. The database URL is configured via the `DATABASE_URL` environment variable (e.g., `sqlite:///path/to/db.db`). Migrations are managed with Alembic.

Is this project still maintained?

No. The README indicates the project has been archived due to faulty project specifications and AI direction that led to endless looping behavior.

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