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.
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.
Enables management of TestMu AI test projects, test cases, test runs, and integration with Jira, HyperExecute, and AI insights through natural language.
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.
MCP server that generates ISO/IEC/IEEE 29119-3 compliant test plan drafts from project information, providing a structured resource for the standard's test plan outline and a tool for draft creation.
Provides accessibility acceptance criteria for 51 web and 42 native components from the MagentaA11y project. Enables searching, retrieving component specs, and generating accessibility documentation in multiple formats including Gherkin syntax and developer notes.
Generates comprehensive API test plans (positive, negative, and boundary/edge cases) from endpoint metadata using LLMs, and exports them as Excel files.
Provides seamless access to the Fake Store API for AI assistants with 18 CRUD tools for managing e-commerce data including products, carts, and users. Perfect for e-commerce demos, testing, and learning MCP development with zero configuration required.
Enables QA/SDET engineers to test APIs by ingesting Swagger/OpenAPI specs and Postman collections, generating and executing tests in multiple languages and frameworks with real-time progress tracking.
An MCP server for reliable iOS Simulator automation that enables agents to control devices, read accessibility UI trees, and capture screenshots. It supports deterministic grounded actions like tapping, typing, and swiping to create a closed-loop observe-reason-act cycle.
Provides a universal bridge to interact with any OpenAI-compatible LLM API (local or cloud), enabling model testing, benchmarking, quality evaluation, and chat operations with performance metrics.