OpenAPI AnyApi MCP Server

The OpenAPI AnyApi MCP Server solves a common pain point for AI‑powered developers: bridging the gap between large, complex OpenAPI specifications and the lightweight, natural‑language interfaces that assistants like Claude expect. Traditional MCP clients struggle to ingest hundreds of kilobytes of JSON, often choking on size or producing errors when the spec is converted to YAML. This server sidesteps those limitations by loading the OpenAPI document in memory and indexing each endpoint as an individual semantic chunk. A MiniLM‑L3 model then powers a fast, in‑memory FAISS vector search that maps a user’s natural‑language query (e.g., “list products”) directly to the most relevant endpoint definition. The result is a fully resolved, parameter‑rich request schema that Claude can hand to a companion tool for execution.

For developers, the value lies in instant, context‑aware API discovery without manual parsing or code generation. The server exposes two primary tools— for retrieving the precise request structure and for executing the call—under a configurable namespace (). This modularity lets teams spin up multiple instances, each pointing to a different service or environment, and expose them under distinct prefixes such as or . The optional allows a single, concise prefix to be added to every tool description, ensuring that Claude selects the correct tool without ambiguity.

Key capabilities include:

• Remote spec loading: No local file management; the server fetches the JSON from a URL and stays current with API changes.
• Semantic endpoint search: MiniLM‑L3 embeddings enable quick, accurate matching of natural language to specific endpoints.
• Endpoint‑level chunking: Each endpoint is treated as a separate document, preserving full context even for large specs.
• FastAPI & async: The underlying web framework guarantees low latency and scalability under concurrent load.
• In‑memory FAISS: Vector search is performed entirely in RAM, delivering microsecond response times for endpoint lookup.

Real‑world scenarios that benefit include internal tooling for private APIs, rapid prototyping of AI assistants that need to call multiple services, and automated data pipelines where an assistant must fetch, transform, and push data across disparate systems. By integrating seamlessly into Claude’s MCP workflow—exposing discovery and execution tools as part of the same namespace—the server enables a fluid handoff from query to call, all within a single conversation.

Unique advantages stem from its lightweight embedding model (43 MB), the ability to handle 100 KB+ OpenAPI documents without loss of context, and its Docker‑ready distribution that pre‑loads the model for zero‑cold‑start latency. While it currently lacks ARM support and has a modest cold‑start cost when not using the pre‑built image, these trade‑offs are offset by the speed and simplicity it brings to AI‑centric API integration.