Loading ToolBench
An autonomous QA, Debugging, and UI/UX Verification MCP Server for Claude AI.
Three tools with basic schemas and descriptions, but significant gaps in parameter documentation, output schema clarity, and error handling. Tool names lack clear action verbs (discover_project_context, execute_playwright_journey are vague). Descriptions exist but are generic and lack WHEN/WHY guidance. Input schemas present but incomplete: execute_playwright_journey's nested 'actions' array lacks proper type constraints on the enum values. No output schemas documented. No error recovery guidance. No security considerations for credential extraction. Baseline: avg tool description ~194 chars (these are 150-180), avg params per tool 4 (these have 1-3), but 0% of params have descriptions beyond the schema.
Deeply analyzes the project to automatically find routes, Playwright selectors, and extract hardcoded seed/env credentials.
Executes a complex series of browser actions (navigate, fill forms, click, wait) and returns a final screenshot and logs. Essential for handling logins and multi-step UI verification.
Starts the local server and waits for it to respond successfully.
Tool names lack action verbs. 'discover_project_context' is vague (discover what? context how?). 'execute_playwright_journey' is generic. Should be 'analyze_project_routes', 'start_dev_server', 'run_browser_actions'. LLMs infer intent from names, vague names cause wrong tool selection.
No parameter descriptions. 'targetPath' appears in all three tools but lacks guidance on format (absolute vs relative? must exist?). 'command' in start_project has no examples or constraints. 'actions' array in execute_playwright_journey has no description of valid action types or required fields per action. LLMs cannot infer parameter meaning from names alone.
Output schemas not documented. discover_project_context returns text logs; start_project and execute_playwright_journey return unknown structures. LLMs cannot plan downstream calls or extract required fields (e.g., port number, screenshot path) without knowing response shape.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | 2026-07-28+ | v2 |
Credential extraction (discover_project_context) exposes sensitive data in logs without sanitization. Emails and password patterns are returned as plain text in the response, which enters LLM context and could be logged/echoed. Violates secret-injection pattern.
No error recovery guidance. If start_project times out or execute_playwright_journey fails, the response provides no actionable next steps. LLM receives raw error message with no classification (retryable? user-fixable? fatal?) or suggestions.