Data Dictionary MCP Server

@jonahkeegan

代码仓库

基本信息

分类

数据与分析

许可证

MIT license

传输方式

stdio

作者

jonahkeegan

关于 Data Dictionary MCP Server

A Model Context Protocol (MCP) server that coordinates AI agents to transform database tables into Wikipedia-style data dictionaries.

#data-dictionary#mcp #ai-agents

Data Dictionary MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "data-dictionary-mcp": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Data Dictionary MCP Server 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。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Data Dictionary MCP?

Data Dictionary MCP is a Model Context Protocol server that automates the conversion of database tables into Wikipedia-style data dictionaries. It uses AI agents coordinated via MCP to analyze, describe, and verify database structures.

How to use Data Dictionary MCP?

Clone the repository, create a Python 3.9+ virtual environment, install dependencies from requirements.txt, then run python src/main.py.

Key features of Data Dictionary MCP

Multi-Format Support: JSON, CSV, and Plain Text files
AI-Powered Analysis: generate field descriptions and relationships
MCP Integration: coordinate AI agents via the protocol
Schema Extraction: unify schemas from various formats
Wikipedia-Style Output: familiar, accessible presentation format

Use cases of Data Dictionary MCP

Create comprehensive data dictionaries from database schemas
Automatically describe fields and identify relationships with AI
Convert JSON, CSV, and plain text into structured dictionaries
Leverage AI agents to verify and improve dictionary accuracy

常见问题

What file formats are supported?

JSON, CSV, and Plain Text are supported; more formats are planned.

What are the prerequisites?

Python 3.9+, Git, and pip or poetry for dependency management.

Is this project production-ready?

No, it is in active development. See the Project Roadmap for details.

How can I contribute?

Contributions are welcome via Pull Requests.

What license does this project use?

MIT License.

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