"OpenAI API Documentation" matching MCP servers:

API Tester AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables natural-language chat-driven API testing, letting users upload API specs, run deterministic pytest checks against live targets, inspect results, create defects, and view failure trends.

Starter scaffold for a workshop to build an MCP API testing server, intended to be extended through incremental steps.

An MCP Server that provides access to Google's Cloud Tool Results API, enabling applications to programmatically interact with test results from Firebase Test Lab and mobile app testing through natural language.

Enables MCP-compatible clients to discover API endpoints from OpenAPI/Swagger specs, send HTTP requests, validate responses, and analyze status codes for automated API testing.

Zero-Config, Fully AI-Managed End-to-End Testing for all code gen platforms.

MCP server for email compatibility analysis. Analyze, preview, diff, and fix HTML emails across 15 email clients — plus capture real screenshots and create shareable links with an optional API key.

Deterministic fluff detector for AI-generated prose. No model, no API key.

MCP server exposing SuperAudit’s free website audit as a callable tool, enabling AI agents to audit any public URL and receive scores across SEO, security, legal, performance, and more. No API key required; respects rate limits and caches results.

Turns natural-language API descriptions into runnable k6 load tests, executes them, and returns structured performance metrics for AI-assisted reporting.

MCP server that enables Claude Code to send code and plans for review by OpenAI Codex, returning structured feedback inline.

MCP server that lets coding agents test AI agents. Create YAML test cases, snapshot golden baselines, check for regressions, and generate visual reports all from inside Claude Code or any MCP-compatible tool. Works with LangGraph, CrewAI, OpenAI, Claude, Mistral, and any HTTP API.

AI-powered continuous code assistant for Cursor, VS Code, Antigravity, and Claude Code via MCP, providing tools for code review, testing, documentation, and quality analysis.

Enables management of TestMu AI test projects, test cases, test runs, and integration with Jira, HyperExecute, and AI insights through natural language.

Validates and tests llguidance grammars (Lark format), providing grammar validation, batch test execution, and syntax documentation.

CLI-first API testing tool with MCP server. Tests are .tarn.yaml; failures come back as structured JSON for AI agents to branch on. Tools: tarn_run, tarn_validate, tarn_fix_plan, tarn_inspect, tarn_rerun_failed.

Enables reading, listing, searching, commenting on, and updating status of ZenTao bugs via its REST API, with write operations defaulting to dry-run.

An MCP server that makes backend API requests and packages request+response pairs as evidence to prove an endpoint works.

Analyzes Playwright test results to provide structured QA insights, including test metrics, failure analysis, bug report drafts, and release-quality recommendations.

An MCP server that automatically discovers API endpoints from any codebase, generates and runs tests, and produces per-role QA audit reports in PDF and XLSX.