Let Your LLM Debug Using Production Recordings

MCP-connected observability can help LLM coding assistants debug production problems by giving them on-demand access to recorded runtime traffic rather than relying only on code analysis and assumptions. The workflow uses Speedscale to record API calls, service interactions, database behavior, and third-party dependencies in production, while the proxymock CLI serves as an MCP bridge that can automatically configure compatible IDEs and assistants such as Cursor. After installing proxymock, configuring its MCP connection, and deploying the Speedscale collector, developers can ask natural-language questions about recent errors, allowing the assistant to inspect the codebase and relevant production requests and responses. In the example, Cursor investigates recurring 500 errors, identifies rate-limit failures from the NASA API, and recommends remedies. The approach is presented as a way to close an observability gap by allowing AI agents to validate hypotheses against actual system behavior, reducing manual evidence gathering and making production data an active debugging and validation resource.

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