"Scraping API endpoints and creating an MCP server" matching MCP servers:

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

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

Enables AI agents to call real API endpoints and verify responses against OpenAPI specs, supporting contract testing, auth presets, spec diffing, and health checks.

Enables creating mock API endpoints and responses, including mock collections and status checks.

Analyzes API requirements and source code, evaluates existing Postman test coverage, and generates or updates test automation with an optional draft pull request on GitHub.

基于fastmcp-ts开发的接口测试mcp工具，ai可以通过调用该工具对接口进行测试。

Enables AI agents to drive API testing from YAML definitions, executing and chaining requests without hand-written curl, while keeping credentials out of the conversation and responses token-cheap, plus a VANDER-based QA review of endpoints.

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.

Enables AI agents to inspect, test, validate, compare, and debug HTTP APIs through a structured toolkit, with features such as secret redaction, response contract validation, regression comparison, health checks, and incident investigation workflows.

Enables benchmarking of Large Language Model APIs by measuring performance metrics such as generation throughput, prompt throughput, and Time To First Token (TTFT) with configurable concurrency levels and parameters.

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

Enables regex testing, replacement, splitting, validation, explanation, escaping, common patterns, and benchmarking through an MCP server.

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.

A full-stack API automation testing server that parses OpenAPI/Swagger/Postman/HAR specs, generates comprehensive test scenarios and executable code, and provides AI-powered review and auto-fix.

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.

Enables instant creation of mock API servers from OpenAPI specs or natural language descriptions, with built-in fake data generation, CRUD endpoints, and configurable delays/errors for testing.

Validates OpenAPI documents, JSON Schemas, and JSON payloads. Also compares OpenAPI specs and displays breaking changes.

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.

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.