How to Use the Honeycomb MCP in OpenAI Agents SDK
Build production observability agents that debug Honeycomb traces autonomously using the OpenAI Agents SDK.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Honeycomb MCP to OpenAI Agents SDK
Create your Vinkius account to connect Honeycomb to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Map your telemetry with the Honeycomb MCP Server
Your OpenAI agent needs context before it fires off queries. It uses `list_datasets` to grab the exact slug for your production environment. From there, it pulls the schema with `list_dataset_columns` so it knows exactly what fields exist in your tracing data. This beats manually clicking through the UI during a P1 incident. The agent checks `get_dataset_details` and instantly understands your data shape. You get faster triage because the system actually knows what to look for before it starts guessing.
Run complex trace analysis autonomously
Writing JSON query specs by hand is terrible. Your agent handles it by calling `create_query_specification` with the exact grouping and filtering you need. It passes that spec to `run_query` and gets a result ID back. Polling is built right into the workflow. The agent hits `get_query_result` until the data comes back. Because you are using the OpenAI Agents SDK, you can set guardrails to ensure the agent never runs unapproved destructive actions while querying.
Annotate deployments and track triggers
Context is everything when debugging a latency spike. You can have your agent fire `create_marker` the second a deployment finishes. It passes the details in the `body_json` parameter, dropping a vertical line right on your team's timeline. The system also tracks active alerts. By calling `list_triggers` and `list_markers`, the agent correlates active pages with recent code changes. It hands that context off to a specialized summarization agent for your post-mortem.
Set up Honeycomb MCP in OpenAI Agents SDK
Prerequisites
- Python 3.10+ installed
-
openai-agentspackage (pip install openai-agents) - Active Vinkius subscription with a valid endpoint token
- 1
Install the SDK
Run
pip install openai-agentsto install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed. - 2
Connect via SSE transport
Use
MCPServerSsewith your Vinkius endpoint URL. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. The SDK auto-discovers all Honeycomb tools at runtime. - 3
Create your Agent
Pass the MCP to
Agent(mcp_servers=[server]). The agent receives Honeycomb tools as native definitions — JSON schemas resolve automatically. - 4
Run the agent
Call
Runner.run(agent, prompt)to execute. The agent invokes the appropriate Honeycomb tools and returns structured results. Copy the full example on the right to get started.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse
async def main():
async with MCPServerSse(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as server:
agent = Agent(
name="Honeycomb Agent",
instructions="You have access to Honeycomb tools.",
mcp_servers=[server],
)
result = await Runner.run(agent, "List recent transactions")
print(result.final_output)
asyncio.run(main())Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Honeycomb. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Common questions about Honeycomb MCP in OpenAI Agents SDK
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