
docketbird-mcp
Server MCP pour API hébergé par Docketbird.
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DocketBird MCP Server
This MCP server provides access to DocketBird's court case data and document management functionality.
Requirements
- Python 3.11
- uv package manager
Setup
- Install uv if you haven't already:
curl -LsSf https://astral.sh/uv/install.sh | sh
- Create and activate a virtual environment:
uv venv
source .venv/bin/activate # On Unix/MacOS
# OR
.venv\Scripts\activate # On Windows
- Install dependencies:
uv pip install .
- Set up your environment variables:
export DOCKETBIRD_API_KEY=your_api_key_here # On Unix/MacOS
# OR
set DOCKETBIRD_API_KEY=your_api_key_here # On Windows
Running the Server
Run the server using:
uv run docketbird_mcp.py --transport stdio # For stdio transport
uv run docketbird_mcp.py --transport sse # For SSE transport
Available Tools
The server provides the following tools:
-
get_case_details
: Get comprehensive details about a case including all documents -
download_document_by_id
: Download a specific document by its DocketBird ID -
list_cases
: Get a list of cases belonging to an account -
list_courts_and_types
: Get a comprehensive list of all available courts and case types
Configuration Files
Make sure these files are in the same directory as the script:
-
courts.json
: Contains information about all available courts -
case_types.json
: Contains information about different types of cases
MCP Server Configuration
The MCP server configuration can be added to one of these locations depending on your MCP client:
- Cursor:
~/.cursor/mcp.json
- Claude in mac:
~/Library/Application Support/Claude/claude_desktop_config.json
- Install uv if you haven't already:
curl -LsSf https://astral.sh/uv/install.sh | sh
Add the following configuration to the appropriate file:
{
"mcpServers": {
"docketbird-mcp": {
"command": "uv",
"args": [
"run",
"--directory",
"PATH_TO_THE_SERVER/docketbird-mcp",
"python",
"docketbird_mcp.py"],
"env": {
"DOCKETBIRD_API_KEY": "YOUR_KEY"
}
}
}
Deployment
The DocketBird MCP server can be deployed to a cloud server using Docker and GitHub Actions. The deployment process is defined in the .github/workflows/deploy.yml
file.
Docker Deployment
The server is containerized using Docker. You can build and run the Docker image locally with the desired transport type:
# Build for ARM architecture (M1/M2 Mac)
docker buildx build --platform linux/arm64 -t docketbird-mcp-arm:latest --load .
# Build for AMD architecture (standard servers)
docker buildx build --platform linux/amd64 -t docketbird-mcp:latest --load .
# Run locally with stdio transport
docker run -d \
--name docketbird-mcp-stdio \
--restart=always \
-e DOCKETBIRD_API_KEY="your_api_key_here" \
-e TRANSPORT_TYPE="stdio" \
docketbird-mcp-arm:latest /app/start.sh
# Run locally with SSE transport
docker run -d \
--name docketbird-mcp-sse \
--restart=always \
-e DOCKETBIRD_API_KEY="your_api_key_here" \
-e TRANSPORT_TYPE="sse" \
docketbird-mcp-arm:latest /app/start.sh
Validating Deployment
To validate that your deployment is working correctly:
- Check that the container is running:
docker ps | grep docketbird-mcp
- Verify the container logs:
docker logs docketbird-mcp
The logs should show:
Starting DocketBird MCP server...
API Key set: your_...
Running python docketbird_mcp.py
- Test the connection from your MCP client using the configuration from this README.
If the container isn't running, you can troubleshoot by checking:
- Docker image exists:
docker images | grep docketbird
- Container logs for errors:
docker logs docketbird-mcp
- Server logs: Check if there are any permission or network issues
# trigger update
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Reviews

user_8rVYJh63
As a dedicated user of docketbird-mcp, I must say this tool has significantly streamlined my workflow. Gravix-db has crafted a robust application that perfectly aligns with my needs. Its seamless integration and user-friendly interface have made managing my projects effortlessly efficient. Highly recommended for anyone looking to optimize their project management process!