"Exploring OpenAPI/Swagger Specifications" matching MCP servers:

MCP server for API test case generation from Swagger/OpenAPI specs. Parses Swagger 2.0 and OpenAPI 3.x, generates test cases across 8 categories (positive, negative, boundary, auth, security, idempotency, pagination, business logic), and exports to Postman, TestRail, Allure, k6, pytest, Gherkin, and CSV. Supports internal corporate APIs with auth headers. Auto-saves export files to your working di

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.

MCP server that analyzes OpenAPI/Swagger REST API contracts and generates BDD Gherkin feature files and TypeScript Jest regression test scripts.

VAST validation MCP server that checks ad tags against IAB VAST specifications, returning structured JSON with rule IDs, XPath locations, and spec references.

Enables AI agents to perform software engineering tasks inside an isolated, deterministic sandbox—exploring repositories, reproducing failures, applying patches, running tests, and verifying solutions against hidden suites through MCP tools.

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

Generates schema-compliant mock data from OpenAPI JSON Schema definitions using AI, enabling seamless testing without manual fixtures.

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 AI coding tools to query and validate analytics event specifications maintained as Wiki markdown tables, providing structured access to events, properties, and implementation details through MCP.

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.

Enables AI assistants to interact with SmartBear's suite of testing and monitoring tools including BugSnag, Swagger, Reflect, and more through natural language, allowing querying of testing data, analysis of performance metrics, and management of test automation.

Enables agents to fetch specifications from the public ProveSpec catalog, search and retrieve specs, and grade their own implementations against capability checklists, returning parity scores and gap lists.

Enables AI assistants to interact with the Scorecard REST API for exploring endpoints, making test requests, and integrating the SDK into applications.

A lightweight server that bridges the MCP protocol with the Atlassian Jira Xray API, enabling safe and configurable access to Xray Server+DC via OpenAPI spec with multiple transports.

MCP server for fts-gate. It enables verification of FTS executable specifications through proof-carrying checks, exposing tools to run gate checks (fts_gate_check) and list available morphisms (fts_morphisms_list), with rejection of invalid proofs via structural logical fallacy detection.

An MCP server that provides curated test cases and behavior specifications for common software features, enabling AI coding agents to define acceptance criteria before writing code.

A local stdio MCP server that lets AI assistants run audits of generic HTML and Shopify product pages, gathering reproducible evidence on identity, structured data, images, links, specifications, and optional browser or link checks. It returns JSON and Markdown reports that the client can interpret, while keeping automated findings separate from checks requiring human judgment.

Analyzes React component changes by performing structural analysis and generating visual diffs to identify pixel-level differences. It integrates with Figma to validate implementation compliance against design specifications and automates component reviews across git branches.

Enables AI assistants to inspect and automate Windows desktop UI elements by exploring UI trees, checking properties, performing actions like clicking and typing, and generating Python automation scripts.