MCP Telemetry

MCP Telemetry

MCP Telemetry is a lightweight Model Context Protocol server designed to capture, organize, and expose detailed conversation logs between users and large language models (LLMs). By integrating with Weights & Biases Weave, it turns every chat session into a rich dataset that can be visualized, queried, and shared—providing developers with an end‑to‑end telemetry pipeline without the need for custom logging code.

Solving the “black‑box” problem

When building AI assistants, developers often struggle to understand how conversations evolve, where failures occur, or how frequently certain tools are invoked. MCP Telemetry automatically records every user utterance, LLM response, tool call, and the resulting output. This granular visibility turns opaque interactions into actionable insights, enabling rapid debugging, performance tuning, and compliance auditing.

What the server does

The server exposes a set of MCP tools that start a tracing session with a custom identifier, log all conversation artifacts, and stream the data in real time to Weights & Biases. Once a session is initiated, every message pair (user ↔ LLM) and any intermediate tool activity are captured as structured events. These events can be visualized in Weights & Biases dashboards, allowing developers to drill down into conversation flows, response latency, error rates, and tool usage patterns—all without modifying the LLM codebase.

Key capabilities

• Session management: Begin and end tracing sessions via simple prompts or programmatic calls, assigning meaningful names for later filtering.
• Comprehensive logging: Capture user inputs, LLM outputs, tool calls, and their results in a single, searchable stream.
• Real‑time analytics: View live updates of conversation progress and tool interactions directly in the Weights & Biases UI.
• Export & sharing: Export logged sessions as structured artifacts that can be shared with stakeholders or used for downstream training data augmentation.

Real‑world use cases

• Quality assurance: QA teams can replay specific conversation traces to reproduce bugs or assess model behavior under edge conditions.
• Model monitoring: Operations teams can set up alerts on latency or error thresholds, ensuring service level objectives are met.
• Research & development: Data scientists can analyze tool usage patterns to inform new feature development or fine‑tuning strategies.
• Compliance & auditing: Regulators can audit conversations for policy adherence, with all interactions preserved in an immutable log.

Integration into AI workflows

Because MCP Telemetry adheres to the Model Context Protocol, it plugs seamlessly into any Claude‑compatible client. Once the server is configured (providing a Weights & Biases API key), it starts automatically with the client, requiring no additional code in the assistant’s logic. Developers can trigger tracing sessions via natural language prompts or by invoking MCP commands, making telemetry a first‑class citizen in the conversational loop.

Unique advantages

Unlike generic logging libraries, MCP Telemetry offers a structured and context‑aware approach tailored to LLM interactions. Its tight coupling with Weights & Biases Weave gives developers powerful visualization tools out of the box, while its MCP interface ensures compatibility with future protocol extensions. The result is a turnkey telemetry solution that scales from prototype chats to production‑grade deployments, giving developers the data they need to build smarter, safer, and more reliable AI assistants.