Loading ToolBench
MCP server for intelligent evaluation of LLM prompts and RAG pipelines with metrics, regression detection, and AI-powered fix suggestions
Eval_MCP has 16 tools with complete input schemas and descriptions present for all tools and parameters. However, descriptions are generic and lack LLM-optimized guidance on WHEN to use each tool vs. alternatives. Output schemas are not documented, responses are inferred from code but not formally specified in tool definitions. Error handling is minimal; no recovery guidance or actionable error messages. Parameter descriptions are functional but lack format constraints, ranges, or dependency documentation. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk classifications (WRITE vs READ_ONLY). Naming is verb-first and clear, but some tools combine multiple concerns (e.g., run_eval_suite handles both dataset selection and prompt selection).
Add annotations/labels to an evaluation run
Compare metric deltas between a baseline and candidate prompt
Detect metric regressions between candidate run and baseline run
Query paginated evaluation run history for a project
Get the latest generated prompt fix suggestion for a run
Get current status and pass rate of an evaluation run
Get list of supported evaluation metrics
Output schemas not documented. Tool definitions lack return type specifications, forcing LLMs to infer response structure from context. This violates pattern:tool and increases hallucination risk when chaining tools.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk classifications. Tools marked WRITE (register_golden_dataset, run_eval_suite, suggest_fix, set_baseline, rerun_failed_cases, annotate_run) should declare destructiveHint=true to guide agent retry logic.
Descriptions lack LLM-optimized guidance on WHEN to use each tool. E.g., run_eval_suite vs score_rag_pipeline both evaluate, but distinction is unclear. Descriptions should state prerequisites, when to call vs. alternatives, and what downstream tools expect.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 55 | 2026-07-28+ | v2 |
List datasets in a project
List all evaluation projects
List prompts in a project
Register a golden dataset of test cases for prompt/RAG evaluations
Rerun failed test cases from a previous evaluation run
Run an evaluation suite over a dataset with specified metrics
Score RAG retrieval & generation pipeline with RAG metrics
Set the baseline run for a project
Queue LLM-as-a-judge to analyze failure clusters and suggest prompt improvements
Parameter descriptions lack format constraints and ranges. E.g., 'run_id' has no format hint; 'dataset_name' has no length constraint. Descriptions should state expected format, length, and allowed values per pattern:constrained-input.
No error handling guidance. Tools return errors but do not tell LLMs what to do next (retry, ask user, call alternative tool). Missing pattern:recovery-guide.