"An example MCP for client prototyping in a business context with a public API call" matching MCP servers:

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

Runs a local stdio MCP server that lets you evaluate an AI agent's business outcome against four synthetic scenarios, including a duplicate-free quote follow-up, an unsent invoice reminder, client-file separation, and a stockout. It scores the run from the final state and recorded attempts rather than the agent's text, returning a pass or fail exit code.

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

Enables MCP clients to open websites, capture screenshots at multiple breakpoints, and run automated design audits for issues like overflow, small tap targets, low contrast, missing alt text, tiny text, and clipped content.

Small diagnostic MCP server for checking what MCP clients actually support, including tools, resources, prompts, and transport behavior.

Enables coding agents to run budgeted tests and checks under a countdown with a stop rule, shape long logs into concise failure summaries, and guard pre-existing test files against silent assertion rewrites. It also delivers relevance-filtered project memory and renders a token-and-time roadmap of the orchestrator and every subagent spawned for a task.

Enables MCP clients to call a single identify tool that returns fixture name, version, transport, and a supplied message for publisher acceptance testing. It runs over stdio without external services or credentials.

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

Enables AI coding agents to perform zero-token static audits, simulate refactorings in an ephemeral RAM clone, trace downstream ripple effects, and block unsafe writes with automatic rollback to prevent regressions.

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

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.

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 assistants to inspect GitHub Actions CI runs, diagnose failures with failed-step summaries and filtered job logs, read per-job coverage, and view pull request checks through read-only, allowlisted access.

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

Enables autonomous agents to automatically repair and guard tool-call schemas through deterministic AST analysis and parameter projection validation, ensuring reliable, schema-compliant interactions in MCP-compatible environments.

Generates a pytest suite from acceptance criteria withheld from the coding agent, so the tests can disagree with the code; qikly_run never returns the criteria. The CLI also scores an existing suite by planting faults, and ships as an agent Skill (qikly --install-skill). Runs locally (uvx --from "qikly[mcp]" qikly-mcp): your code and API key stay on your machine.

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.

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.