A self-evolving Cursor MCP with comprehensive tools and analytics
Critical failures across naming, descriptions, and schemas. 28 tools registered but many lack proper definitions or are duplicated (query_llm, web_scrape, take_screenshot appear 2-3 times). Descriptions are present but generic (avg ~80 chars, well below 194-char baseline). Parameter descriptions often missing or trivial. No output schemas documented. Tool names lack clear verb-noun structure (e.g., 'query_llm' is acceptable, but 'setup', 'learn_lesson', 'save_to_memory' are vague). Many tools expose dangerous operations (db_query, batch_process, lint_code with --fix) without dry-run or confirmation patterns. No error handling guidance visible in schemas.
Analyze dependencies in a project
Process multiple files with a text transformation
Comprehensive code analysis tool with multiple capabilities
Search for code patterns or strings in files
Create a new database from a SQL script
Execute a SQL query on a database
Get the schema of a database
Duplicate tool definitions: query_llm, web_scrape, and take_screenshot are registered multiple times with conflicting schemas. LLMs will be confused about which variant to call.
Destructive operations lack dry-run or confirmation: db_query (WRITE), lint_code (--fix), batch_process (regex replace), smart_refactor all modify state without confirmation pattern. Agents can cause data loss.
No output schemas documented. Tools return data but LLMs cannot plan downstream calls without knowing response structure. E.g., does code_search return file paths, line numbers, both? Does web_crawler return URLs, content, metadata?
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 46 | 2026-07-28+ | v2 |
Find files matching pattern with advanced options
Retrieve information from persistent memory
Analyze an image using computer vision capabilities
Record a lesson learned for future improvement
Lint code files for common issues
Generate insights about project structure, patterns, and architectural recommendations
Query a large language model with a prompt
Query a large language model with a prompt
Save information to persistent memory
Search the web using a search engine
Search the web and extract semantically relevant information based on a query
Install, configure, verify, or uninstall MCP servers
Intelligent code refactoring with architecture awareness
Take a screenshot of a webpage
Take a screenshot of a webpage
Convert text to speech audio
Watch files or directories for changes
Crawl a website to extract structured information from multiple pages
Scrape content from a web page
Scrape content from a webpage
Search the web for information
Generic tool names reduce clarity: 'search' (vs web_search), 'setup' (install? configure? both?), 'learn_lesson' (store? analyze? unclear action). LLMs struggle to disambiguate intent.
Parameter descriptions missing or trivial for 40+ parameters. E.g., 'The path to the file' (db_path, file_path) lacks format/constraint info. 'The action to perform' (setup) doesn't explain what each action does. LLMs cannot validate inputs.