Building a Better MCP Server and Proving It with HDX Evals

ClickHouse describes improvements to the ClickStack MCP server and evaluates the result with HDX Evals. The article appears to focus on MCP tool design, evaluation methodology, and evidence-based validation for agent interactions with observability systems. It is relevant to engineers building AI agents that query operational data, because MCP quality depends on more than exposing an API: tool boundaries, schemas, prompts, and measurable task success all matter. The available metadata does not include the detailed benchmark setup or results, so those claims should be checked in the original post.