Honeybadger MCP Server

A Model Context Protocol (MCP) server implementation for interacting with the Honeybadger API. This server allows AI agents to fetch and analyze error data from your Honeybadger projects.

Overview

This MCP server provides a bridge between AI agents and the Honeybadger error monitoring service. It follows the best practices laid out by Anthropic for building MCP servers, allowing seamless integration with any MCP-compatible client.

Features

The server provides two essential tools for interacting with Honeybadger:

1. list_faults: List and filter faults from your Honeybadger project

   • Search by text query
   • Filter by creation or occurrence timestamps
   • Sort by frequency or recency
   • Paginate results

2. get_fault_details: Get detailed information about specific faults

   • Filter notices by creation time
   • Paginate through notices
   • Results ordered by creation time descending

Prerequisites

• Python 3.10+
• Honeybadger API key and Project ID
• Docker if running the MCP server as a container (recommended)

Installation

Using uv

1. Install uv if you don't have it:

   pip install uv
   

2. Clone this repository:

   git clone https://github.com/bobtista/honeybadger-mcp.git
   cd honeybadger-mcp
   

3. Install dependencies:

   uv pip install -e .
   

4. Install development dependencies (optional):

   uv pip install -e ".[dev]"
   

5. Create your environment file:

   cp .env.example .env
   # Edit .env with your configuration
   

Using Docker (Recommended)

1. Build the Docker image:

   docker build -t honeybadger/mcp --build-arg PORT=8050 .
   

2. Create a .env file and configure your environment variables

Configuration

You can configure the server using either environment variables or command-line arguments:

Running the Server

Running with uv (Development)

SSE Transport (Default)

# Using environment variables:
HONEYBADGER_API_KEY=your-key HONEYBADGER_PROJECT_ID=your-project uv run src/honeybadger_mcp_server/server.py

# Using CLI arguments:
uv run src/honeybadger_mcp_server/server.py --api-key your-key --project-id your-project

Using Stdio

uv run src/honeybadger_mcp_server/server.py --transport stdio --api-key your-key --project-id your-project

Running Installed Package

SSE Transport (Default)

# Using environment variables:
HONEYBADGER_API_KEY=your-key HONEYBADGER_PROJECT_ID=your-project honeybadger-mcp-server

# Using CLI arguments:
honeybadger-mcp-server --api-key your-key --project-id your-project

Using Stdio

honeybadger-mcp-server --transport stdio --api-key your-key --project-id your-project

Using Docker

Run with SSE

docker run --env-file .env -p 8050:8050 honeybadger/mcp

Using Stdio

With stdio, the MCP client itself can spin up the MCP server container, so nothing to run at this point.

Integration with MCP Clients

SSE Configuration

Once you have the server running with SSE transport, you can connect to it using this configuration:

{
  "mcpServers": {
    "honeybadger": {
      "transport": "sse",
      "url": "http://localhost:8050/sse"
    }
  }
}

Claude Desktop Configuration

Using SSE Transport (Recommended)

First, start the server:

honeybadger-mcp-server --api-key your-key --project-id your-project

Then add to your Claude Desktop config:

{
  "mcpServers": {
    "honeybadger": {
      "transport": "sse",
      "url": "http://localhost:8050/sse"
    }
  }
}

Using Stdio Transport

Add to your Claude Desktop config:

{
  "mcpServers": {
    "honeybadger": {
      "command": "uv",
      "args": [
        "run",
        "--project",
        "/path/to/honeybadger-mcp",
        "src/honeybadger_mcp_server/server.py",
        "--transport",
        "stdio",
        "--api-key",
        "YOUR-API-KEY",
        "--project-id",
        "YOUR-PROJECT-ID"
      ]
    }
  }
}

Docker Configuration

{
  "mcpServers": {
    "honeybadger": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "honeybadger/mcp",
        "--transport",
        "stdio",
        "--api-key",
        "YOUR-API-KEY",
        "--project-id",
        "YOUR-PROJECT-ID"
      ]
    }
  }
}

Tool Usage Examples

List Faults

result = await client.call_tool("list_faults", {
    "q": "RuntimeError",           # Optional search term
    "created_after": 1710806400,  # Unix timestamp (2024-03-19T00:00:00Z)
    "occurred_after": 1710806400, # Filter by occurrence time
    "limit": 10,                  # Max 25 results
    "order": "recent"             # 'recent' or 'frequent'
})

Get Fault Details

result = await client.call_tool("get_fault_details", {
    "fault_id": "abc123",
    "created_after": 1710806400,  # Unix timestamp
    "created_before": 1710892800, # Optional end time
    "limit": 5                    # Number of notices (max 25)
})

Development

Running Tests

# Install dev dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

Code Quality

# Run type checker
pyright

# Run linter
ruff check .

Contributing

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