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AgentWong_Optimized-Memory-MCP-Serverv2
镜像://github.com/agentwong/optimized-memory-mcp-serverv2
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optimized-memory-mcp-serverv2
This is a personal project to test Claude AI's ability to self-write an MCP Server code for its own use.
This is a Python-based Model Context Protocol (MCP) server implementation designed to work with Claude Desktop as an MCP client.
Project Structure
mcp_server/
├── README.md # Project documentation
├── requirements.txt # Project dependencies
├── server.py # Main server implementation
├── tests/ # Test directory
│ ├── __init__.py
│ ├── test_resources.py
│ └── test_tools.py
├── docs/ # Documentation
│ └── CONVENTIONS.md # Implementation conventions
└── src/ # Source code
├── __init__.py
├── resources/ # Resource implementations
│ └── __init__.py
├── tools/ # Tool implementations
│ └── __init__.py
└── utils/ # Utility functions
└── __init__.py
Getting Started
-
Ensure Python 3.13.1 is installed:
python --version # Should show 3.13.1
-
Install uvx if not already installed:
pip install uvx
-
Clone this repository:
git clone https://github.com/AgentWong/optimized-memory-mcp-serverv2.git cd optimized-memory-mcp-serverv2
-
Create and activate a virtual environment:
python -m venv .venv source .venv/bin/activate # On Unix/macOS # or .venv\Scripts\activate # On Windows
-
Install dependencies:
pip install -r requirements.txt pip install -r requirements-dev.txt # For development
-
Initialize the database:
alembic upgrade head
-
Run the server:
uvx run python -m src.main
Development
Follow the conventions outlined in docs/CONVENTIONS.md
for implementation details and best practices.
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Reviews

user_UNJ6IeVr
I've been using AgentWong_optimized-memory-mcp-serverv2 and it's truly impressive. Developed by MCP-Mirror, this server enhances memory optimization significantly, resulting in smoother performance. For any MCP applications, this is a game-changer. Highly recommend checking it out at https://github.com/MCP-Mirror/AgentWong_optimized-memory-mcp-serverv2.