$ loading_
Run prompt and RAG evaluations through MCP clients with hosted backend execution.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Eval_MCP" yet — see the docs or source repo.
Use Eval_MCP to evaluate two customer-support prompt versions, compare their accuracy, stability, and style consistency, and recommend the better one.
A comparison of evaluation results, key metric scores, and a recommended prompt version.
Use Eval_MCP to assess our RAG QA workflow, focusing on retrieval hit rate, answer relevance, and hallucinations, then summarize the main issues.
An evaluation report for the RAG workflow, including retrieval and generation performance, issue diagnosis, and optimization suggestions.
Guide me to use Eval_MCP in Claude Desktop or Cursor to register, create an API key, and run a batch of prompt evaluation tasks.
Clear setup steps or invocation flow to help the user configure the tool and start batch evaluations.
Build, debug, and manage software tasks with natural language across LLMs.
Run LLM evaluations, experiments, and custom evaluators through a standardized MCP interface.
Evaluate AI agent outputs for CI gates, regressions, and canary promotions.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.
Expose modular retrieval and reasoning tools to AI assistants through MCP.
Demo MCP server for calculations, time checks, notes, and code review prompts.