Honeycomb MCP

Overview

The Honeycomb Model Context Protocol (MCP) server provides a seamless bridge between large language models—such as Claude—and the rich observability data stored in Honeycomb. By exposing Honeycomb’s analytics, SLOs, triggers, and dataset metadata through the MCP interface, developers can let an AI assistant query, interpret, and act upon real‑time telemetry without leaving the conversational or code environment. This eliminates the need to manually export logs, write custom API wrappers, or maintain separate dashboards for troubleshooting and insight generation.

At its core, the server runs as a single process that listens on standard input/output streams. When an MCP‑enabled client issues a request, the server translates it into Honeycomb API calls, applies caching for non‑query endpoints to reduce latency and API usage, and streams the results back in a structured format. Because the server requires only an API key with broad permissions, it can access data across multiple environments (e.g., production and staging) by simply configuring environment variables in the MCP configuration file. EU users can point to the regional endpoint, ensuring compliance with data residency requirements.

Key capabilities include:

• Multi‑environment querying: Run the same analytic query against different Honeycomb instances by mapping environment names to distinct API keys.
• Rich analytics support: Leverage Honeycomb’s calculation types (COUNT, AVG, P95, etc.), filters, and time‑based aggregations directly from the AI interface.
• Metadata exploration: Retrieve dataset schemas, board configurations, SLO definitions, and trigger settings without manual API calls.
• Caching: Intelligent in‑memory caching of static resources (datasets, columns, boards) reduces round trips and speeds up repeated queries.

Real‑world use cases abound: a DevOps engineer can ask an AI assistant to “show me the 95th percentile latency for the last 24 hours in production” and receive a concise chart or summary; a product manager can request the latest SLO compliance metrics across environments to inform release decisions; or an incident responder can quickly pull trigger histories and related markers to triage outages. In each scenario, the MCP server removes friction by allowing AI agents to act as a first‑line analyst that can pull, interpret, and even suggest remediation steps based on Honeycomb data.

Integration into existing AI workflows is straightforward. Once the MCP server is registered in a client’s configuration, any supported tool—Claude Desktop, Claude Code, Cursor, Windsurf, Goose, or others—can issue standard MCP calls. The server’s stateless design and use of environment variables for credentials make it easy to embed in CI/CD pipelines, local development environments, or containerized services. Developers benefit from a single, consistent API surface that unifies observability data access with the conversational power of modern LLMs, accelerating debugging, monitoring, and decision‑making across teams.