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Evaluate code in a sandbox with automated execution and LLM-based quality scoring.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-eval-harness" yet — see the docs or source repo.
Use mcp-eval-harness to run this Python code in a sandbox, verify it against the provided test cases and edge cases, and score it for readability, robustness, and efficiency with improvement suggestions.
Returns test results, pass/fail status, quality scores, and specific improvement suggestions.
Use mcp-eval-harness to execute these two implementations of the same feature, compare output correctness, error handling, and code quality, and conclude which version is better for production.
Provides a comparative evaluation, detailed scores, and a recommendation between the two versions.
Use mcp-eval-harness to assess this LLM-generated code: run it in a sandbox first, then score functional completeness, stability, security risks, and maintainability, and identify potential issues.
Generates an execution validation report, risk notes, and an overall quality score.
Run prompt and RAG evaluations through MCP clients with hosted backend execution.
Run commands, manage long jobs, and transfer files in AI sandboxes.
Evaluate MCP agent outputs with standardized quality, safety, and cost scoring.
Use a sandboxed interface to run reverse engineering tools securely.
Run dev checks and get compact error summaries for faster debugging.
Run code in a secure sandbox to cut tokens and protect data privacy.