Use a full HTTP client to test APIs, compare environments, and infer types.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "curl-curl-cool-mcp" yet — see the docs or source repo.
Send a GET request to this API: https://api.example.com/users/42. Show the status code, headers, a response body summary, and point out any potentially problematic fields.
A concise API response report with analysis of the response structure or suspicious fields.
Request the /orders endpoint from my staging and production environments, compare response schemas, key field differences, and status codes, then summarize possible compatibility issues.
A diff-style report highlighting environment mismatches, useful for diagnosing config or API inconsistencies.
Call this user profile endpoint and infer TypeScript type definitions from the live response. Clearly mark optional fields and nested objects as well.
Reusable TypeScript interfaces or type declarations with clear field structure.
Run a production-ready MCP server for files, HTTP, system info, and environment tools.
Read, edit, and summarize documents through a Claude-powered chat interface.
Delegate web research and image generation to ChatGPT through browser automation.
Return personalized greetings to demonstrate basic MCP tool integration.
Turn CLI tools or REST APIs into MCP servers for Claude.
Deploy MCP servers over HTTP for AI-accessible text, math, and content tools.