MCP OpenAPI Proxy

The MCP OpenAPI Proxy is a specialized server that bridges the gap between traditional RESTful APIs defined by OpenAPI specifications and the Model Context Protocol (MCP) used by AI assistants such as Claude. By automatically parsing an OpenAPI document and generating the corresponding MCP resources, tools, prompts, and sampling mechanisms, it removes the need for developers to hand‑craft MCP servers from scratch. This automation accelerates adoption of MCP in existing microservice ecosystems, allowing AI agents to query and manipulate services without bespoke integration work.

At its core, the proxy listens for MCP requests from an AI agent and forwards them to the underlying OpenAPI server. It translates MCP tool calls into HTTP requests that conform to the original API contract, then maps the responses back into the MCP format expected by the assistant. The result is a seamless experience: an LLM can invoke any operation defined in the OpenAPI spec—such as creating a user, retrieving inventory data, or updating a record—as if it were calling a native MCP tool. The proxy also handles authentication, rate limiting, and error translation, ensuring that the AI receives clear, actionable feedback when something goes wrong.

Key capabilities include:

• Dynamic MCP generation from any OpenAPI spec, eliminating manual code writing.
• Transparent request routing, so the AI agent interacts with a single MCP endpoint while the proxy forwards to multiple underlying services.
• Support for streaming transports (e.g., ), enabling real‑time data feeds to the assistant.
• Configurable environment variables for host, port, and spec URL, making deployment flexible across containers, Kubernetes, or serverless platforms.

Typical use cases span a wide range of scenarios:

• Sidecar deployment: Run the proxy alongside an existing microservice to expose its API to AI assistants without modifying business logic.
• Rapid prototyping: Quickly generate MCP tooling for new services during development sprints, allowing designers to test LLM interactions early.
• Enterprise integration: Expose legacy REST APIs to modern AI workflows, enabling chat‑based dashboards or voice assistants that can manipulate enterprise data.

Integrating the MCP OpenAPI Proxy into an AI workflow is straightforward. A developer first supplies the URL of the target OpenAPI spec and configures any necessary authentication headers. The proxy then exposes a single MCP endpoint that the AI assistant can query. When the assistant sends a tool request, the proxy translates it into an HTTP call to the original API, captures the response, and returns it in MCP format. This abstraction lets developers focus on business logic while giving AI agents the power to interact with complex services as if they were native tools.

In summary, the MCP OpenAPI Proxy offers a low‑friction path from conventional REST APIs to AI‑friendly MCP interfaces, delivering immediate value for developers who want to unlock the full potential of conversational agents without rewriting their backend services.