Data Dictionary MCP

by jonahkeegan

276 downloads Not rated yet MIT license

GitHub

About

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

Details

License: MIT license

Explore

- Multi-Format Support: Process JSON, CSV, and Plain Text files (with more formats planned)
- AI-Powered Analysis: Generate field descriptions and identify relationships
- MCP Integration: Coordinate AI agents using the Model Context Protocol
- Schema Extraction: Extract database schemas from various formats into a unified representation
- Wikipedia-Style Output: Present data dictionaries in a familiar, accessible format

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Data Dictionary MCP

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

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

JSON

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

JSON

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

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

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

JSON

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

JSON

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

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

From the repository

- Python 3.9+
- Git
- pip or poetry for dependency management

- Develop web interface
- Implement search capabilities
- Add user feedback system
- Create deployment infrastructure

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

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

McpServers

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

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

Overview

The Data Dictionary MCP project automates the conversion of various database formats into comprehensive, human-readable data dictionaries using AI-powered analysis and description. It leverages the Model Context Protocol (MCP) to coordinate AI agents for analyzing, describing, and verifying database structures.

Features

Project Status

This project is in active development. See the Project Roadmap for details.

Getting Started

Prerequisites

- Python 3.9+
- Git
- pip or poetry for dependency management

Installation

1. Clone the repository:

   git clone https://github.com/jonahkeegan/data-dictionary-mcp.git

   cd data-dictionary-mcp

2. Create a virtual environment:

   python -m venv venv

   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   python src/main.py

Project Structure

data-dictionary-mcp/
├── docs/                  # Documentation
├── src/                   # Source code
│   ├── mcp/               # MCP server components
│   ├── analyzers/         # Format analyzers
│   ├── agents/            # Agent coordination
│   └── dictionary/        # Dictionary generation
├── tests/                 # Test suite
├── memory-bank/           # Cline memory bank
├── .gitignore
├── .clinerules            # Cline rules
├── README.md
└── requirements.txt

Project Roadmap

Milestone 1: MCP Server Foundation and Format Analyzers

- Implement MCP server with basic tool definitions - Develop format analyzers for JSON, CSV, and Plain Text - Create schema extraction system - Implement unit tests for core components

Milestone 2: AI Agent Coordination and Field Description

- Implement agent coordination system - Develop field description generation - Create task distribution and result aggregation - Add integration tests

Milestone 3: Content Verification and Publishing

- Implement content validation - Develop Wikipedia-style formatting - Create export capabilities - Add end-to-end tests

Milestone 4: User Interface and Deployment

- Develop web interface - Implement search capabilities - Add user feedback system - Create deployment infrastructure

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is open source and available under the MIT License.

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About

Details

Explore

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Overview

Features

Project Status

Getting Started

Prerequisites

Installation

Project Structure

Project Roadmap

Milestone 1: MCP Server Foundation and Format Analyzers

Milestone 2: AI Agent Coordination and Field Description

Milestone 3: Content Verification and Publishing

Milestone 4: User Interface and Deployment

Contributing

License

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