Meta API MCP Server

Meta API MCP Server is a gateway that bridges any HTTP‑based API with Model Context Protocol (MCP) clients such as Claude, GPT, or Cursor. By exposing APIs through MCP, the server lets AI assistants treat external services as first‑class tools—sending requests, receiving structured responses, and integrating real‑world data directly into conversational workflows. This eliminates the need for custom wrappers or serverless functions, dramatically reducing latency and simplifying deployment.

The core value of the server lies in its universal API ingestion capability. Developers can load JSON configuration files that describe endpoints, HTTP methods, parameters, and authentication schemes, or even import entire Postman collections. The server parses these inputs automatically, converts them into MCP‑compatible tool definitions, and serves them on a single endpoint. This means that thousands of ready‑made APIs—weather services, e‑commerce platforms, analytics dashboards—can be made available to an LLM with a single command. The tool also supports multiple authentication methods (API key, bearer token) and can fetch configuration data from local files or remote URLs, making it flexible for CI/CD pipelines and multi‑environment deployments.

Key features include:

• Multi‑API orchestration – manage dozens of services from one server instance.
• Zero‑code configuration – add or edit APIs using simple JSON files or the web‑based MCP API Editor.
• Postman collection conversion – automatically translate existing collections into MCP tools, preserving folders, variables, and authentication.
• HTTP method support – full CRUD (GET, POST, PUT, DELETE, PATCH) handling with automatic parameter extraction.
• Environment variable integration – secure API keys and tokens are injected via environment variables, keeping secrets out of configuration files.

Typical use cases span from building chat‑based dashboards that pull live data, to automating order fulfillment workflows in e‑commerce, or enabling a conversational bot that can query internal analytics APIs. In an AI workflow, the server sits between the LLM and external services; the assistant invokes a tool by name, the server translates it into an HTTP request, and returns the parsed response back to the model. This tight coupling allows developers to focus on intent modeling rather than plumbing, and it opens up new possibilities for dynamic content generation and real‑time decision making.

Unique advantages of Meta API MCP Server are its automatic Postman conversion—a rare feature that eliminates manual tool definition—and its single‑command launch from both CLI and MCP clients. The server’s lightweight Node.js implementation ensures it can run locally, in Docker containers, or on cloud functions with minimal overhead. For developers looking to expose external APIs to AI assistants quickly and securely, Meta API MCP Server provides a ready‑made, configurable gateway that scales from prototypes to production deployments.