Authored by Alan Shimel, this article features insights from Honeycomb CEO Christine Yen on how Model Context Protocol (MCP) servers enhance AI-driven observability and the evolving landscape for DevOps teams.

AI-Driven Observability: Fast, Context-Rich MCP Servers

By Alan Shimel

Introduction

Christine Yen, developer-turned-CEO of Honeycomb, discusses the company’s foray into building an MCP (Model Context Protocol) server and suggests that similar solutions may soon become standard for observability vendors. MCP servers serve as intelligent intermediaries for AI agents, making a product’s API, telemetry schema, and utility tools easily discoverable. This enables large-language models (LLMs) to pose precise, context-aware questions instead of operating on assumptions.

What is Model Context Protocol (MCP)?

MCP is described by Yen as akin to a concierge for AI agents. Instead of requiring manual prompt engineering or memorizing the intricacies of various APIs and telemetry structures, agents can use the MCP server to:

Discover APIs and their capabilities
Access up-to-date telemetry schemas
Leverage helper tools for data analysis and troubleshooting

This increased discoverability enables both humans and AI models to interact with complex observability data in more natural, efficient ways.

The Need for Speed in AI-Driven Observability

Yen emphasizes that, for LLM-driven troubleshooting to be viable, the underlying system must perform quickly. Engineers expect near-instant query turnaround—not multi-minute delays. Honeycomb’s practice of building for speed (“fast is a feature”) bears fruit when agents or users need results from dozens of sub-queries during activities such as root-cause analysis. The faster the queries, the snappier and more effective conversational answers become.

Contextual Depth: Moving Beyond Dashboards

Traditional observability solutions often lock users into static metric dashboards. Honeycomb takes a different approach—customers ingest richly annotated event streams (e.g., checkout_latency_ms, user_tier) that provide additional business context. This means LLMs and other AI agents can:

Map natural language queries to highly descriptive fields
Pinpoint meaningful anomalies without exhaustive prompt engineering

Yen argues this context-driven model offers better results than working with limited, inflexible metric sets.

AI Assistance and Human Synergy

Yen reflects on her own experience, noting she no longer remembers every field in Honeycomb’s systems, but the MCP server does. She predicts that new users will increasingly rely on AI agents as copilots for observability, while power users may choose between natural language and raw query interfaces. The challenge for vendors is to support both workflows simultaneously, empowering all types of users.

Best Practices and Future Outlook

Yen’s advice to engineers and organizations:

Begin experimenting with AI agents and context protocols now
Document and map out key sources of operational context
Demand open protocols and standards from tool providers

She concludes that the future of observability stacks is not simply collecting data, but serving it rapidly and in the right context—whether the recipient is human or AI.

Video Interview: Watch on YouTube