How to integrate Honeycomb MCP with Pydantic AI

Connect Pydantic AI to Honeycomb MCP. Investigate latency spikes in production dataset, compare error rates by endpoint today, and more using natural language, with authentication handled for you.

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Honeycomb is an observability platform for high-cardinality event data. It helps teams debug production systems fast with rich telemetry analysis.

32 Tools

Introduction

This guide walks you through connecting Honeycomb to Pydantic AI using the Composio tool router. By the end, you'll have a working Honeycomb agent that can investigate latency spikes in production dataset, compare error rates by endpoint today, find anomalous traces in checkout service through natural language commands.

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

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

Also integrate Honeycomb MCP 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 Honeycomb
  • 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 Honeycomb 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 Honeycomb MCP server, and what's possible with it?

The Honeycomb MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Honeycomb account. It provides structured and secure access so your agent can perform Honeycomb operations on your behalf.

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 Honeycomb
  • 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 Honeycomb
  • MCPServerStreamableHTTP connects to the Honeycomb 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 Honeycomb
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["honeycomb_mcp"],
    )
    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 Honeycomb 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
honeycomb_mcp_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[honeycomb_mcp_mcp],
    instructions=(
        "You are a Honeycomb assistant. Use Honeycomb tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Honeycomb endpoint
  • The agent uses GPT-5 to interpret user commands and perform Honeycomb 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 Honeycomb.\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
  • Honeycomb 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 Honeycomb MCP 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 Honeycomb
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["honeycomb_mcp"],
    )
    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
    honeycomb_mcp_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[honeycomb_mcp_mcp],
        instructions=(
            "You are a Honeycomb assistant. Use Honeycomb 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 Honeycomb.\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 Honeycomb through Composio's Tool Router. With this setup, your agent can perform real Honeycomb 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 + Honeycomb 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 Honeycomb MCP action and event your agent gets out of the box.

Canvas agent invoke

Kick off a single-turn run of the Honeycomb Canvas agent.

Canvas agent poll response

Poll for the result of a previously-issued canvas_agent_invoke call.

Create board

Create a board (dashboard) with query, SLO, and text panels.

Create marker

Creates a marker (a vertical annotation on charts) to mark a point or window in time, such as a deploy, incident, or config change.

Create recipient

Create a notification recipient (email, Slack, PagerDuty, or webhook) so it can be attached to triggers and SLO burn alerts.

Create slo

Create an SLO (Service Level Objective) backed by an auto-created SLI derived column.

Create trigger

Create a trigger that fires alerts when a query result crosses a threshold.

Feedback

Submit feedback about Honeycomb's MCP server to the agentic-intelligence team.

Find columns

Search for columns and calculated fields by intent across one or all datasets in an environment.

Find queries

Search query history and saved queries by intent; returns matching queries with their run PKs.

Get aiconversation

Fetch the full event timeline for a single AI conversation, identified by its OpenTelemetry gen_ai.

Get dataset

Get dataset metadata and its full column schema (columns + calculated fields), sorted by most recent write activity.

Get dataset columns

Get the full column schema for one dataset, with optional sample values for specific columns.

Get environment

Get details for a specific environment, including its dataset list sorted by most recent activity.

Get query results

Retrieve results and metadata from an existing query execution.

Get semconv attribute

Get the full definitions of one or more semantic convention attributes by their exact names.

Get span details

Summarize attributes and their common values observed on spans with a specific name.

Get trace

Retrieve all spans for a specific trace ID and render them as a waterfall.

Get triggers

List triggers (alert rules) for the team, or fetch full configuration for a single trigger.

Get workspace context

Call this tool first to orient yourself in a Honeycomb workspace.

List aiconversations

Discover recent AI agent conversations (gen_ai.

List boards

List boards (saved dashboards) in an environment, or fetch one board's full contents by ID.

List recipients

List all pre-registered notification recipients (email, Slack, PagerDuty, webhook) for the team.

List semconv namespaces

List the top-level semantic convention namespaces available in this team's registry.

List spans

List span names in trace data, ranked by count, with how often each is a trace root and which dataset the count came from.

Refinery docs

Read Honeycomb Refinery documentation.

Run bubbleup

Run BubbleUp analysis to find what makes a selected data subset different from the baseline.

Run query

Run a time-series aggregation query against a Honeycomb dataset and return computed results.

Search semconv

Search the semantic convention registry for attributes matching a query.

Update board

Edit an existing board (dashboard) in place.

Update slo

Update an existing SLO.

Update trigger

Update an existing trigger.

FRAMEWORKS

How to build Honeycomb MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Honeycomb MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Honeycomb MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Honeycomb MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Honeycomb MCP with Claude Cowork

Codex logo

Codex

Use Honeycomb MCP with Codex

OpenClaw logo

OpenClaw

Use Honeycomb MCP with OpenClaw

Hermes logo

Hermes

Use Honeycomb MCP with Hermes

Google ADK logo

Google ADK

Use Honeycomb MCP with Google ADK

LangChain logo

LangChain

Use Honeycomb MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Honeycomb MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Honeycomb MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Honeycomb MCP with LlamaIndex

CrewAI logo

CrewAI

Use Honeycomb MCP with CrewAI

AutoGen logo

AutoGen

Use Honeycomb MCP with AutoGen

FAQ

Frequently asked questions

With a standalone Honeycomb MCP server, the agents and LLMs can only access a fixed set of Honeycomb tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Honeycomb 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 Honeycomb tools.

Yes, absolutely. You can configure which Honeycomb 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 Honeycomb data and credentials are handled as safely as possible.

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