Honeycomb MCP Server for OpenAI Agents SDK 12 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Honeycomb through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Honeycomb Assistant",
instructions=(
"You help users interact with Honeycomb. "
"You have access to 12 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Honeycomb"
)
print(result.final_output)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Honeycomb MCP Server
Connect your Honeycomb.io observability platform to any AI agent and take full control of your telemetry data, query specifications, and incident markers through natural conversation.
The OpenAI Agents SDK auto-discovers all 12 tools from Honeycomb through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Honeycomb, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.
What you can do
- Dataset Oversight — List all event sources, retrieve detailed metadata, and monitor last access times for your datasets.
- Query Management — Define new query specifications and execute them to retrieve granular performance insights.
- Marker Automation — Create timeline annotations (e.g., for deployments or outages) to contextualize your data visualization.
- Schema Insights — List and inspect columns within specific datasets to understand your event structure.
- Team Collaboration — Access shared boards and retrieve information about your Honeycomb team configuration.
- Incident Analysis — Use AI to run complex queries and retrieve results for rapid troubleshooting and RCA.
The Honeycomb MCP Server exposes 12 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Honeycomb to OpenAI Agents SDK via MCP
Follow these steps to integrate the Honeycomb MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 12 tools from Honeycomb
Why Use OpenAI Agents SDK with the Honeycomb MCP Server
OpenAI Agents SDK provides unique advantages when paired with Honeycomb through the Model Context Protocol.
Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Honeycomb + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Honeycomb MCP Server delivers measurable value.
Automated workflows: build agents that query Honeycomb, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents — one queries Honeycomb, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Honeycomb tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Honeycomb to resolve tickets, look up records, and update statuses without human intervention
Honeycomb MCP Tools for OpenAI Agents SDK (12)
These 12 tools become available when you connect Honeycomb to OpenAI Agents SDK via MCP:
create_marker
Pass details as a JSON string in "body_json" (requires message). Use "__all__" for team-wide markers. Create a new marker (e.g., deploy, maintenance) on a dataset timeline
create_query_specification
Pass the specification as a JSON string in "query_json". Returns a query ID for execution. Create a new query specification for a dataset
get_dataset_details
Get metadata for a specific dataset
get_query_result
Retrieve the results of an executed query
get_team_details
Retrieve information about the Honeycomb team
list_dataset_columns
List all columns (fields) defined in a specific dataset
list_datasets
Use this to find the "slug" required for markers and queries. List all datasets in your Honeycomb team
list_honeycomb_boards
List all boards (dashboards) shared with the team
list_markers
List markers (annotations) for a dataset
list_queries
List query specifications for a specific dataset
list_triggers
List triggers (alerts) defined for a dataset
run_query
Poll for results using "get_query_result" with the returned result ID. Execute a query specification and return a result ID
Example Prompts for Honeycomb in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Honeycomb immediately.
"List all datasets and find one related to 'payment-gateway'."
"Create a marker on all datasets: 'Deploy v2.4.0 started'."
"Execute query ID 'q_99283' for the 'main-api' dataset."
Troubleshooting Honeycomb MCP Server with OpenAI Agents SDK
Common issues when connecting Honeycomb to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Honeycomb + OpenAI Agents SDK FAQ
Common questions about integrating Honeycomb MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Honeycomb with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Honeycomb to OpenAI Agents SDK
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.