Loading ToolBench
A custom benchmark runner skill for evaluating AI models against test datasets, measuring accuracy and performance metrics across reasoning, code synthesis, and tool call reliability.
This MCP server has severe definition quality issues. The single tool 'run_benchmark_eval' lacks proper documentation, has minimal parameter descriptions, and provides no structured output schema documentation. The server appears to be a proof-of-concept or stub implementation rather than a production-ready MCP server.
Run benchmark evaluation on a test dataset against a target model endpoint
Tool name uses generic verb 'run' instead of action-specific verb like 'execute_', 'trigger_', or 'evaluate_'. Generic names make it hard for LLMs to infer intent.
Tool description is 83 characters, which is below the baseline average of 194 chars. It lacks context about when to use this tool, what prerequisites exist, and what the output represents.
Parameter 'test_dataset_path' has only a brief description (48 chars) with no guidance on expected format, file size limits, supported formats, or how to structure the JSONL file. LLM cannot infer proper usage.
Parameter 'target_model_endpoint' has only a 46-char description with no guidance on valid format, resolution strategy, or error handling if endpoint is unreachable. Default value 'gpt-5.6-luna' appears fabricated and may confuse LLMs.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 32 | 2026-07-28+ | v2 |
Output schema is not documented. The tool returns a dict with nested structure (accuracy_score_pct, total_evals_passed, benchmark_report with reasoning_accuracy, code_synthesis, tool_call_reliability), but LLM has no visibility into this structure for downstream planning.
Implementation is a mock returning hardcoded values (94.8% accuracy, 284 evals passed). Tool does not actually invoke any evaluation logic or external model endpoints. This is a non-functional stub.
No error handling documented. Tool has no try/catch, no validation of input paths, no guidance on what happens if dataset is malformed or endpoint is invalid. Agents cannot recover from failures.
No input validation. Accepts arbitrary file paths with no bounds checking, no sanitization against path traversal, no verification that file exists or is readable.
Default parameter value 'gpt-5.6-luna' looks like a placeholder and does not match any known OpenAI, Anthropic, or standard model naming scheme. This will mislead LLMs into using an invalid endpoint by default.
No pagination, result limits, or streaming support documented. If benchmark results are large, tool could blow context window. No guidance on max items returned.