Overview of the @reapi/mcp-openapi MCP Server

The @reapi/mcp-openapi server solves a common pain point for developers building AI‑powered IDEs: exposing rich, machine‑readable API contracts to large language models (LLMs) without manual integration. By ingesting a directory of OpenAPI 3.x specifications, the server translates these definitions into the Model Context Protocol (MCP) format. This bridge lets tools like Cursor, VS Code extensions, or any MCP‑compatible editor present the full API surface—endpoints, parameters, request/response schemas—to an LLM in real time. The result is a developer experience where the assistant can understand, generate, and validate API calls directly inside the editor.

At its core, the server loads multiple OpenAPI files simultaneously and builds a catalog of available APIs. Each operation becomes an MCP tool that the LLM can invoke, while dereferenced schemas provide complete context so that generated code is type‑safe and compliant. Developers benefit from instant access to API documentation, schema validation, and operation discovery—all without leaving the code editor. This reduces context switching, speeds up onboarding for new APIs, and ensures that generated client code matches the latest contract.

Key capabilities include:

• Bulk spec loading: A single directory can contain dozens of YAML or JSON files, and the server will parse and expose them all.
• Dereferenced schemas: Internal references are resolved, giving the LLM a fully expanded view of request and response structures.
• Catalog maintenance: The server keeps an up‑to‑date inventory of all APIs, allowing the assistant to list available services or operations on demand.
• MCP tool integration: Each endpoint is exposed as an actionable tool that the LLM can call, returning formatted request payloads or example responses.

Typical use cases span a wide range of workflows. In a microservices architecture, a developer can ask the assistant to “create a request for the POST endpoint” and immediately receive a fully typed payload. QA teams can generate test cases by querying the catalog for all endpoints and letting the LLM draft assertions. New team members can quickly explore an API’s capabilities through conversational prompts, eliminating the need to read lengthy documentation. The server also plays well with CI/CD pipelines: tests or code generation scripts can invoke the MCP tools programmatically to keep client libraries in sync with evolving APIs.

Integration is straightforward. The server can be configured per‑project or globally via Cursor’s MCP settings, ensuring that the correct set of specifications is available in each context. When specs change, a simple chat command refreshes the catalog, keeping the assistant’s knowledge current. The combination of local OpenAPI loading and real‑time LLM interaction gives developers a powerful, low‑friction way to harness AI for API development and testing.