Data Dictionary MCP Server

@jonahkeegan

Basic information

About 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 config

Add this server to your MCP-compatible client using the configuration below.

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

Data Dictionary MCP Server tools

Tool list not available yet

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We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

Frequently asked questions

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.

Comments

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