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How to integrate Honeybadger MCP with Pydantic AI

Connect Pydantic AI to Honeybadger MCP. Report a new deployment to honeybadger, upload javascript source maps after release, and more using natural language, with authentication handled for you.

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Honeybadger is an error tracking and application monitoring service for developers. It helps you spot, resolve, and get notified about app issues—before your users even notice.

7 Tools

Introduction

This guide walks you through connecting Honeybadger to Pydantic AI using the Composio tool router. By the end, you'll have a working Honeybadger agent that can report exceptions and events, record deployments, send check-ins, and upload source maps through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Honeybadger account through Composio's Honeybadger MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Honeybadger with

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Honeybadger
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Honeybadger workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the Honeybadger MCP server, and what's possible with it?

The Honeybadger MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Honeybadger account. It provides structured and secure access to your error monitoring and deployment data, so your agent can perform actions like reporting exceptions, tracking deployments, sending custom events, and managing source maps on your behalf.

  • Error and exception reporting: Instantly notify Honeybadger of new exceptions or critical errors by sending detailed diagnostic data, including stack traces and context information, for fast troubleshooting.
  • Automated deployment tracking: Let your agent report new deployments to Honeybadger after every release, so you always have up-to-date context for error tracking and performance monitoring.
  • Scheduled task monitoring: Use the agent to report check-ins (pings) for scheduled jobs, ensuring your background tasks are running reliably and on time.
  • Custom telemetry and event logging: Send structured NDJSON events to Honeybadger Insights, allowing you to capture and analyze application-specific metrics and events.
  • Source map and file uploads: Upload JavaScript source maps and supporting files to Honeybadger for improved error de-minification and debugging of production errors.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Honeybadger
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Honeybadger
  • MCPServerStreamableHTTP connects to the Honeybadger MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Honeybadger
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["honeybadger"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Honeybadger tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
honeybadger_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[honeybadger_mcp],
    instructions=(
        "You are a Honeybadger assistant. Use Honeybadger tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Honeybadger endpoint
  • The agent uses GPT-5 to interpret user commands and perform Honeybadger operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Honeybadger.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Honeybadger API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Honeybadger and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Honeybadger
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["honeybadger"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    honeybadger_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[honeybadger_mcp],
        instructions=(
            "You are a Honeybadger assistant. Use Honeybadger tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Honeybadger.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Honeybadger through Composio's Tool Router. With this setup, your agent can perform real Honeybadger actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Honeybadger for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Honeybadger action and event your agent gets out of the box.

Report Check-In

Reports a check-in (ping) to Honeybadger for uptime monitoring.

Report Check-In With Payload

Report a check-in to Honeybadger with execution status, duration, stdout, stderr, or exit code.

Report Deployment

Report a new deployment to Honeybadger for deployment tracking and error correlation.

Report Event

Send custom events to Honeybadger Insights for tracking, monitoring, and analytics.

Report Exception

Tool to report an exception notice to Honeybadger.

Upload File to S3

Tool to upload a local file to a managed S3 bucket.

Upload Source Map

Upload JavaScript source maps to Honeybadger for error stack trace de-minification.

FRAMEWORKS

How to build Honeybadger MCP Agent with another framework

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Claude Code

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Claude Cowork

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OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

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Codex logo

Codex

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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Atomic Agent logo

Atomic Agent

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Google ADK logo

Google ADK

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LangChain logo

LangChain

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

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LlamaIndex logo

LlamaIndex

Use Honeybadger MCP with LlamaIndex

CrewAI logo

CrewAI

Use Honeybadger MCP with CrewAI

AutoGen logo

AutoGen

Use Honeybadger MCP with AutoGen

FAQ

Frequently asked questions

With a standalone Honeybadger MCP server, the agents and LLMs can only access a fixed set of Honeybadger tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Honeybadger and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Honeybadger tools.

Yes, absolutely. You can configure which Honeybadger scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Honeybadger data and credentials are handled as safely as possible.

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