Loading ToolBench
A comprehensive collection of Python and shell-based skills, agents, and tools for code generation, analysis, and automation. Includes dependency auditing, agent design, browser automation, database optimization, and system hooks.
Critical gaps across all dimensions. While tool_schema_generator shows sophisticated internal logic (ParameterType, ValidationRule enums, format validators), the MCP server exposes tools with severely incomplete definitions. Input schemas are present but descriptions are generic/boilerplate. No output schemas documented. Parameters lack type constraints and validation guidance. Error handling is absent. The server appears to be a skill repository wrapper rather than a production MCP implementation. Code sample shows Python dataclasses but no evidence of actual MCP tool registration, handler implementation, or error recovery patterns.
Multi-Agent System Performance Analysis. Takes agent execution logs (task, actions taken, results, time, tokens used) and evaluates performance: task success rate, average cost per task, latency distribution, error patterns, tool usage efficiency, identifies bottlenecks and improvement opportunities.
Multi-Agent System Architecture Designer. Given a system description (goal, tasks, constraints, team size), designs a multi-agent architecture: defines agent roles, responsibilities, capabilities needed, communication topology, tool requirements. Generates architecture diagram (Mermaid).
Database Index Optimizer. Analyzes schema definitions and query patterns to recommend optimal indexes: identifies missing indexes for common query patterns, detects redundant and overlapping indexes, suggests composite index column ordering, estimates selectivity and performance impact, generates CREATE INDEX statements with rationale.
Generates Playwright scraping script skeletons. Takes a URL pattern and CSS selectors as input and produces a ready-to-run Playwright scraping script with pagination support, error handling, and anti-detection patterns baked in.
Generate structured tool schemas for AI agents. Given a description of desired tools (name, purpose, inputs, outputs), generates structured tool schemas compatible with OpenAI function calling format and Anthropic tool use format. Includes: input validation rules, error response formats, example calls, rate limit suggestions.
No output schemas documented for any tool. LLMs cannot plan downstream calls or extract required fields. Violates pattern:tool and pattern:response-shaper.
Input parameter descriptions are minimal (10-30 chars) and lack format/constraint guidance. E.g., 'schema' param in index_optimizer has no guidance on structure, required fields, or format. Violates pattern:tool-description baseline of 72 chars avg.
No error handling or recovery guidance. Tools lack error_responses, retryability classification, or actionable error messages. Violates pattern:recovery-guide and pattern:error-classification.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 31 | 2026-07-28+ | v2 |
Tool descriptions are generic boilerplate (30-50 chars) lacking WHEN/WHY context. E.g., scraping_toolkit says 'Generates Playwright scraping script skeletons' but omits when to use vs alternatives, prerequisites, or output format details.
No pagination, result limits, or batching support documented. Tools accepting complex objects (schema, queries, execution_logs) lack guidance on max size, result caps, or chunking strategies. Violates pattern:paginated-result.