Honeycomb's MCP: Revolutionizing AI Observability & Debugging
Honeycomb has unveiled an ambitious concept for the evolving field of observability with its new Model Context Protocol (MCP). As observability expands in both its application and its integration with artificial intelligence, Honeycomb’s MCP aims to seamlessly weave AI capabilities directly into the development environment, making debugging and operational analysis more intuitive and efficient.
At its core, MCP facilitates the integration of chosen AI models, such as Cursor, Claude Code, or VS Code, directly into an Integrated Development Environment (IDE). This allows developers and operations teams to query their systems for insights, debug issues, or analyze performance directly within their coding interface. Honeycomb CEO Christine Yen describes this as a solution that “solves agent context issues elegantly and accelerates AI-assisted debugging workflows,” effectively creating a dedicated MCP for Honeycomb queries, dubbed ICP.
According to Honeycomb’s documentation, the system allows an AI agent to investigate issues like a latency spike by prompting it within the IDE. The agent then leverages MCP to execute Honeycomb queries and remotely analyze trace data – detailed records of operations that reveal system behavior. A key design principle of MCP tooling is to prevent “chat context overload,” a common pitfall for AI models overwhelmed by excessive information. Features like column search and trace view ensure that AI agents retrieve only the most relevant telemetry, the data collected from monitoring systems.
Austin Parker, Honeycomb’s Director of Open Source, elaborated that the MCP server can access a comprehensive array of resources within a user’s environment, including dashboards, triggers, Service Level Objectives (SLOs), and queries. When the MCP server operates within a compatible client like Claude Desktop, VS Code, or Cursor, AI agents can be given open-ended tasks and utilize these tools to achieve their objectives.
Parker offered compelling examples of MCP in action. If an SLO—a target for system performance or reliability—is showing signs of degradation, a Cursor agent can inspect that SLO and conduct investigations within Honeycomb. It can then combine this data with an analysis of the codebase to identify and rectify bugs or enhance performance. A particularly innovative application involves instructing an AI agent to improve the instrumentation of a new or existing service. The agent can use Honeycomb to identify specific idioms and attributes already in use across other services, then apply these established patterns when modifying code. Furthermore, MCP excels at using Honeycomb data in conjunction with other contextual information, such as OpenTelemetry Semantic Conventions, to pinpoint opportunities for telemetry refactoring, like converting existing log-based telemetry into more structured spans.
Despite its promise, the development of the MCP server has presented significant challenges, primarily concerning the sheer volume of data returned by Honeycomb’s query API. Parker noted that anything beyond the most basic queries can generate an overwhelming amount of “tokens”—the units of text processed by large language models (LLMs). With some Honeycomb accounts containing tens of thousands of columns, thousands of triggers, and hundreds of datasets, it becomes “extremely easy for an agent to get itself in a doom loop of queries and hallucinations where it constantly forgets the name of attributes, gets confused about dataset names, and more.”
This challenge, however, extends beyond Honeycomb. Other Software as a Service (SaaS) tools integrating similar MCP servers and AI capabilities are likely to encounter comparable issues. The fundamental problem lies in the unique design of each LLM interface; the kind of structured JSON API response suitable for programmatic access is often ill-suited for direct consumption by an LLM. Here, MCP servers offer a crucial abstraction layer, enabling developers to modify responses in transit. This allows for simplification of data structures and removal of unneeded fields before the information reaches the LLM, mitigating the risk of overwhelming the AI and ensuring more accurate, context-aware interactions.