What prerequisites are needed?
Python 3.8+, uv (modern Python package manager), npx (included with Node.js), and a PostgreSQL database you can connect to.
@NetanelBollag
基本情報
A beginner-friendly MCP server template featuring a PostgreSQL connector with clean, easy-to-understand code. Perfect for developers new to Model Context Protocol who want to experiment and create their own AI tool connectors with minimal setup.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"simple-psql-mcp": {
"command": "uv",
"args": [
"venv"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
It is a template project for developers wanting to build their own MCP servers, designed to be dead simple to understand and adapt. It implements a PostgreSQL MCP server that provides tools, resources, and prompts, allowing LLMs to interact with a PostgreSQL database through natural language queries.
Prerequisites: Python 3.8+, uv, npx, and a PostgreSQL database. Set up a virtual environment, install dependencies, then run the server with the MCP Inspector using npx @modelcontextprotocol/inspector uv --directory . run postgres -e DSN=... -e SCHEMA=public. Alternatively, configure an AI assistant (e.g., Claude Desktop) with a JSON configuration file, or use the included generate_mcp_config.sh script. Once connected, ask the LLM questions in natural language about your data.
execute_query tool – run SQL queries against the databasetest_connection tool – verify the database connectiondb://tables, db://tables/{table_name}, db://schemaPython 3.8+, uv (modern Python package manager), npx (included with Node.js), and a PostgreSQL database you can connect to.
Set the `DSN` (e.g., `postgresql://username:password@hostname:port/database`) and `SCHEMA` environment variables when running the inspector or in the MCP configuration file.
Yes. Run the included script `./example-db/create-db.sh` to create a Docker container with a PostgreSQL database pre-populated with sample users and addresses tables.
Yes. Create a new directory under `/src`, implement your MCP server following the PostgreSQL example, and add your entry point to `pyproject.toml`. Then run it with `npx @modelcontextprotocol/inspector uv --directory . run your-mcp-name`.
No. This is an experimental project; it only checks that the query starts with `SELECT`, making SQL injections easy. Do not run in production unless you are the founder and there are no paying clients.
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