"How to Run a Python Script" matching MCP servers:

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Run the requests in a Postman collection folder and get structured, assertion-level results back straight from your AI assistant.

Automated QA for .NET microservice backends and React frontends, enabling API contract testing, UI crawling, YAML flows, and run diffing against a stage environment.

A stdio MCP server that audits other MCP servers over the live protocol. It connects to any MCP target (stdio or HTTP), lints every tool's schema for agent-usability, then actually calls the tools with deliberately broken inputs to see how the server handles them, and returns a 0–100 conformance score with a per-dimension breakdown rendered as Markdown.

Enables testing how different MCP clients handle tool results containing both content and structuredContent, including outputSchema enforcement and mismatch scenarios.

A read-only API change deploy gate for AI coding agents that analyzes OpenAPI specs to determine if changes are safe to deploy. It provides tools for comparing specs, explaining breaking changes, and generating migration guides.

Enables AI agents to create, run, and manage HTTP requests using the Invoker API client's .ivk format. It provides tools for scanning collections, executing requests, managing environments, and building documentation sites.

An MCP server for interacting with Treasure Data API, allowing users to retrieve database information and check server status through natural language queries.

MCP server for Mockzilla. Lets coding agents like Claude Code, Claude Desktop, Cursor and Gemini CLI install the Mockzilla CLI and run API mocks from an OpenAPI spec or a single endpoint. Log in from the agent to deploy hosted mocks on mockzilla.org

Probes your live API and classifies why each endpoint failed (root cause, evidence, and a calibrated confidence level), exposed over MCP so your AI assistant debugs from evidence instead of guessing. Works with FastAPI, Express, Next.js, tRPC, and GraphQL.

Enables AI agents to discover and read Typeship API documentation and execute API operations through schema-validated MCP tools, with optional read-only mode and configurable result limits.

A generic MCP server that dynamically exposes any OpenAPI-documented REST API to LLMs by auto-discovering endpoints. It provides tools for exploring API capabilities and making authenticated requests directly through natural language interfaces.

Converts any OpenAPI/REST API into MCP tools with dynamic server switching, variable substitution, and runtime configuration. Enables seamless interaction with multiple API environments through natural language without pre-configuration.

A TypeScript-based MCP server that enables AI assistants to execute HTTP requests with full control over methods, headers, and body content. It facilitates seamless interaction with web APIs and endpoints through a versatile tool that handles automatic JSON formatting and response truncation.

A reusable MCP server for QA automation testing that enables running tests across web apps, APIs, CLI tools, and more directly from Claude Desktop.

Python MCP server for running and inspecting Bruno API collections

Scaffold, validate, format, and run YAML .mmt API tests.

HAL (HTTP API Layer) is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models.

Enables AI agents to send HTTP requests to any endpoint with full control over methods, headers, query parameters, and request bodies.

Enables AI assistants to automatically manage Apifox API documentation by importing OpenAPI/Swagger specifications and exporting existing API structures. Supports batch operations, intelligent deprecation marking, and smart scope detection for partial module imports.