"Setting up an MCP server in Kubernetes" matching MCP servers:

Goodday‑MCP is a lightweight Model Context Protocol (MCP) server designed to seamlessly integrate with the Goodday project management platform via its API v2. It enables querying of projects, tasks, and users—without altering any data—making it ideal for secure context-aware applications

A Node In Layers package that simplifies creation of MCP (Model-Control-Protocol) servers with tools for defining models, adding CRUD operations, and interacting with clients.

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.

TrustPilot for APIs, built for AI agents. Independent reliability ratings for APIs and MCP servers — look up trust scores, compare providers side by side, and leave reviews from real agent traffic.

Optimizes REST API calls for AI agents by returning compact schemas instead of full responses, allowing agents to fetch only needed fields via jq masks, reducing token consumption by up to 50x.

Turns an existing Express, Hono, Fastify, or Next.js API into a Streamable HTTP MCP endpoint, exposing one tool per OpenAPI operation that runs in-process through the app's existing routes, auth, validation, and handlers. It also serves discovery documents and readiness guidance so models can better understand and call the API.

Lets an AI assistant learn the CloudCart Storefront GraphQL API, look up types, queries and mutations by name, and validate GraphQL snippets against schema and common real-world mistakes. It runs entirely locally with no store, token, or network access, so no live data is ever touched.

Provides a check_links tool that lets AI apps verify up to 200 URLs at once, returning status codes, redirect chains, final URLs, and connection errors.

MCP server that exposes RESTForge capabilities to AI agents, enabling them to set up, configure, generate code, and manage RESTForge projects through natural language.

MCP server for reading, writing, and organizing API documentation in Apidog.

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

An MCP server for Tern that enables the generation, verification, and management of webhook handlers across over 16 platforms and various frameworks. It provides tools to create verified endpoints, debug signature failures, and manage failed event replays via dead letter queues.

Enables MCP hosts to list, look up, and describe a static catalog of OpenAPI operations, and to inspect a governing contract and explicit deny-list that define what the server refuses to do.

Runs, tests and edits the saved HTTP requests in a Kurlo API workspace (Git-backed YAML): list collections and environments, send requests with their auth and pm.* tests, write new requests and tests, and diagnose the last failure. Secret values are sent to the API but masked in every tool result, and protected environments are refused.

An MCP server that generates MCP servers. Give it an API definition - OpenAPI 3.0/3.1 or a Postman collection or extend an existing server - and it scaffolds a complete, runnable, ownable TypeScript or Python MCP server for that collection of APIs.

An MCP server that enables LLMs to make arbitrary HTTP requests (GET, POST, PUT, DELETE, etc.) with custom headers, bodies, and cookies, supporting JSON and error handling.

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

A small native HTTP client whose MCP server (plunger mcp) lets an AI agent send requests safely: undefined variables are refused, secrets are filled in by name and masked in results, and every request is recorded in a history you can see in the window. MIT licensed, for Windows, with a Linux build of the CLI and MCP server.

Point Claude at any API. JitAPI figures out which endpoints to call and in what order — automatically. Register any OpenAPI spec, search endpoints in plain English, and orchestrate multi-step workflows across multiple APIs. No API keys required — works out of the box with local embeddings.

A generic MCP (Model Context Protocol) server that bridges any GraphQL API to Claude Code. It introspects your GraphQL schema and exposes each query and mutation as an individual tool, letting Claude interact with your API directly.

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.

Enables AI tools to inspect configured rulesets and safely validate YAML or JSON rulesets, with planned support for linting OpenAPI, AsyncAPI, and Arazzo documents and looking up rules.

Enables AI coding agents to create webhook bins, inspect and replay HTTP requests, and stand up mock API endpoints directly from the editor.

Enables LLMs to dereference and query OpenAPI/Swagger specifications, search endpoints and schemas, validate payloads, extract security schemes, and generate production-ready integration code in multiple languages.

An MCP server that indexes the public-apis catalogue, allowing LLMs to search and filter over 1,400 public APIs by category, authentication type, and technical requirements. It facilitates precise API discovery for developers by providing tools to query specific features like HTTPS support and CORS compatibility.

Turns an OpenAPI spec into a ready-to-run test harness with local LLM agents: WireMock stubs, contract tests, robustness probes, and chaos scenarios you can switch on and off in a running WireMock.

An MCP server for interacting with MockServer, enabling AI assistants to create mock HTTP expectations, verify requests, clear state, and manage MockServer instances programmatically.

Enables AI agents to create, run, and save API requests and collections as local Gud API files, which are git-committable and viewable in VS Code-compatible editors.

Enables AI coding assistants to discover, inspect, and run API requests in Bruno collections locally, keeping all data private.