Loading ToolBench
A Model Context Protocol server for dynamically optimizing agent prompts, specifications, and skill code using MIPROv2 or GEPA optimization techniques with LLM-based evaluation and dataset generation.
Server has 11 tools with basic schemas and descriptions, but significant quality gaps. All tools have descriptions (positive), but most are generic and lack actionable detail for LLM selection. Parameter descriptions are minimal or missing context. No output schemas documented. Error handling is present but generic ('Error: ...'). No tool annotations (readOnlyHint/destructiveHint). Many tools combine multiple concerns (e.g., optimize_agent_file accepts both optimizer_type and document_dir, mixing algorithm selection with data sourcing). Parameter naming inconsistent: some use full paths (agent_markdown_path), others use abbreviated forms. No enum constraints on optimizer_type despite accepting only 'mipro' or 'gepa'. Security concern: no validation shown for file paths (potential traversal). Composition weak: tools like optimize_entire_skill_directory and optimize_with_optimize_anything appear to duplicate functionality.
Move all pipeline-generated artifacts to a timestamped archive folder.
Generate a synthetic training dataset from an agent markdown file. Supports document_dir for supplementary reference docs, LLM-as-judge curation, and multiple output formats (json, jsonl, alpaca, chatml).
Dynamically optimizes an agent prompt file using MIPROv2 or GEPA. Optionally accepts a document_dir for supplementary reference docs. optimizer_type: "mipro" (default) or "gepa".
Optimizes an agents markdown file by processing each section independently.
Optimizes all Python skill files in a directory using MIPROv2 or GEPA.
Optimizes a prompt specifically for a target model using GEPA's optimize_prompt pipeline.
No output schemas documented. Tools return generic string responses ('Error: ...', result strings) with no structured field definitions. LLMs cannot plan downstream calls or extract specific data.
optimizer_type parameter accepts only 'mipro' or 'gepa' but is defined as free-form string with no enum constraint. LLMs may hallucinate invalid values.
File path parameters (agent_markdown_path, spec_json_path, skill_file_path) lack validation against path traversal. Code checks existence but not canonicalization. Potential security risk.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 45 | <=2025-11-25 | v2 |
Optimizes Python skill code using MIPROv2 or GEPA with pytest validation. optimizer_type: "mipro" (default) or "gepa".
Optimizes a specification JSON file using MIPROv2 or GEPA.
Optimizes any artifact (markdown, Python, JSON, YAML, etc.) using GEPA's optimize_anything pipeline.
Validates a generated dataset against an agent markdown file using LLM-based validation.
Verifies prompt generalization by running QA verification against an agent markdown file.
Duplicate/overlapping tool functionality: optimize_agent_file, optimize_with_optimize_anything, and optimize_prompt_for_target_model all perform optimization. LLM must reason about which to use.
Error messages are generic ('Error: ...') and do not guide recovery. No actionable next steps or categorization (retryable vs fatal).