"Facebook Live" matching MCP servers:

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.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

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.

An MCP server that gives AI coding agents real-time access to the public-apis dataset, enabling search, live probing, and code snippet generation for 1,500+ free public APIs.

Enables AI assistants to help developers build on the monday.com GraphQL API by validating queries, explaining errors, providing column value formats, introspecting the live schema, executing queries, and scaffolding boilerplate code.

Enables agents to verify real-world assertions like content being posted or PRs merged by fetching live evidence from URLs and GitHub, returning a verified boolean plus raw evidence.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.

A lightweight MCP server for searching SmugMug documentation and calling the live SmugMug API.

Native, read-only MCP bridge exposing live Charles Proxy traffic to MCP clients like Claude Code, Codex and Cursor

Enables AI agents to connect to Charles Proxy for live and historical traffic analysis, summary-first insights, request detail inspection, and data-driven mock creation via Map Local/Remote.

An MCP (Model-Controller-Processor) server for accessing League of Legends client data. This server provides a collection of tools that communicate with the League of Legends Live Client Data API to retrieve in-game data.

Bridges with an installed API monitoring application to enable launching and status checks, live background capture, process attach, filter-tree inspection, GUI actions, and opening, validating, decoding, searching, inspecting, exporting, and comparing saved captures and related metadata.

Enables agents to discover and route tasks to public MCP tools, check live reliability, find working alternatives, audit server agent-readiness, and scan tool definitions for poisoning.

Lets an AI assistant run locally over stdio to inspect and test the Klap Core API — looking up charges and timelines, listing webhooks and deliveries, checking accepted token/network pairs, querying metrics, and, in test mode, creating charges, re-checking them on-chain, retrying deliveries and driving sandbox payments. Live environments are read-only unless writes are explicitly opted into, and money-moving or endpoint-management operations are never exposed.

Enables coding agents to discover, write, and test integrations for LinkedIn (Classic, Sales Navigator, Recruiter), WhatsApp, Instagram, Telegram, Gmail/Outlook/IMAP email, and Google/Outlook calendar by reading the live API contract and executing real calls on connected accounts.

Enables AI coding agents to build, debug, and test hosted mock REST APIs by scaffolding persistent CRUD resources, importing OpenAPI specs, simulating requests, auto-healing failing endpoints, and injecting latency or error faults. It also exposes live OpenAPI and TypeScript contract resources for each workspace.

Enables a coding agent to turn an OpenAPI/Swagger file or a recorded browser session (HAR) into a curated, tested MCP server project with ordinary Python code, tests, CI, and publish workflows. It exposes tools to inspect the source, draft and redesign a workflow-level tool plan, validate it, generate the project, and verify every tool with live calls to detect API drift.

Enables AI agents and MCP clients to dynamically add, update, and remove Mock Service Worker network mocks via MCP tools, including live handler updates without restarting the application source code.

Enables AI coding assistants to inspect OpenAPI specs, fuzz live REST endpoints with boundary and adversarial vectors, replay failing anomalies deterministically without AI tokens, and scaffold OpenAPI specs from source routes.

Enables ChatGPT plugins and other MCP clients to fetch live web pages and documents as Markdown and to call HTTP APIs via self-hosted Cloudflare Workers.