Docketbird MCP

🚀 DocketBird MCP Server

DocketBird MCP Server is a powerful tool for case information management and retrieval. It provides various functions such as case information query and document management, and can be easily deployed through Docker.

🚀 Quick Start

✨ Prerequisites

• Python 3.11 or higher
• uv package manager

📦 Installation

Install uv Package Manager

Run the following command to install uv:

curl -L https://github.com/uv-algo/uv/archive/v2.0.0.tar.gz | tar xz && cd uv-2.0.0 && make install

Create and Activate a Virtual Environment (Recommended)

Unix/MacOS

Create and activate a virtual environment:

python -m venv venv && source venv/bin/activate

Windows

Create and activate a virtual environment:

python -m venv venv && .\venv\Scripts\activate

Install Dependencies

Use the following command to install project dependencies:

uv pip install .

Set Environment Variables

Set the DOCKETBIRD_API_KEY environment variable:

export DOCKETBIRD_API_KEY=your_api_key_here  # Unix/MacOS
set DOCKETBIRD_API_KEY=your_api_key_here     # Windows

💻 Usage Examples

Standard Output (stdio) Mode

Run the following command to start the server:

python docketbird_mcp.py

Server-Sent Events (SSE) Mode

Run the following command to start the server:

python docketbird_mcp.py --synchronous

✨ Features

Case Information Query

• get_case_details: Retrieve detailed case information.
• list_documents: List documents in a case.

Document Management

• retrieve_document: Retrieve a specific document.
• search_content: Search within document content.

📚 Documentation

Configuration Files

• courts.json: Defines supported court information.
• case_types.json: Defines case type classifications.

MCP Server Configuration

General Configuration Example

{
  "servers": [
    {
      "url": "http://165.227.221.151:8040",
      "api_key": "your_api_key_here"
    }
  ]
}

Cursor Configuration Example

{
  "docket_bird_url": "http://localhost:8000",
  "api_key": "your_api_key_here"
}

Docker Deployment

Build a Docker Image

Run the following command to build the image:

docker build -t docketbird_mcp .

Start the Container (ARM Architecture)

docker run --name docketbird-mcp-arm \
  -e DOCKETBIRD_API_KEY=your_api_key_here \
  -d docketbird_mcp:arm

Start the Container (AMD64 Architecture)

docker run --name docketbird-mcp-amd64 \
  -e DOCKETBIRD_API_KEY=your_api_key_here \
  -d docketbird_mcp:x86

Verify Deployment

1. Check the container running status:

docker ps | grep docketbird-mcp

2. View the logs:

docker logs docketbird-mcp

💻 DocketBird Agent Prototype

Features

• Provides an interactive command-line interface.
• Supports natural language queries for case information.
• Connects to the deployed DocketBird MCP Server.

Setup and Run

1. Ensure that the OpenAI API key is set:

export OPENAI_API_KEY=your_openai_api_key_here  # Unix/MacOS
set OPENAI_API_KEY=your_openai_api_key_here     # Windows

2. Run the agent program:

cd agents
python db_agent_prototype.py

Example Queries

• "Please provide detailed information about case txnd-3:2007-cv-01697."
• "What documents are available in this case?"
• "When was the latest filing in this case submitted?"

Dependency Requirements

• OpenAI API key (for GPT-4.1 model)
• Internet connection (to access the deployed MCP Server)
• Python dependencies: pydantic_ai, termcolor, python-dotenv

Note: This agent prototype is connected to the following MCP Server:

http://165.227.221.151:8040