MCP Swagger Server

MCP Swagger Server (mss) is a zero‑configuration bridge that turns any OpenAPI/Swagger specification into a fully‑functional Model Context Protocol (MCP) server. By parsing the standard REST API definition, it automatically exposes each operation as an MCP tool that AI assistants can call directly. This eliminates the need for manual wrapper code, allowing developers to expose existing services to AI agents with a single command line invocation or a lightweight configuration file.

The server’s core value lies in its ability to translate complex API schemas into AI‑friendly prompts and response formats. Once the OpenAPI document is supplied, mss generates a catalog of tools that include operation names, required parameters, and expected output schemas. These tools are then served over multiple transport layers—standard input/output for local execution, Server‑Sent Events (SSE) for streaming responses, or a dedicated streamable protocol—ensuring compatibility with the diverse deployment environments of AI assistants.

Key capabilities include:

• Zero‑config conversion: A single flag or URL suffices to spin up the MCP server; no boilerplate code is required.
• Progressive CLI wizard: Interactive prompts guide users through optional filtering (HTTP methods, paths, status codes) and authentication setup.
• Multi‑protocol support: Choose between stdio, SSE, or streamable transports to match the client’s expectations.
• Bearer token authentication: Secure APIs can be protected by passing a token directly or via environment variables, keeping credentials out of the command line.
• Operation filtering: Fine‑grained control over which endpoints are exposed, enabling focused toolsets for specific workflows.

In practice, mss empowers scenarios such as:

• Rapid prototyping where a data scientist can expose an internal analytics API to a language model without writing adapters.
• Secure enterprise integrations where the same toolset can be shared with multiple AI assistants while enforcing bearer‑token authentication.
• Hybrid cloud deployments where the MCP server runs locally on a developer’s machine but exposes services hosted in a remote Kubernetes cluster, all through the same CLI.

Integrating with AI workflows is straightforward: the MCP server can be launched as a local process or deployed behind a reverse proxy, and AI assistants like Claude Desktop or other MCP‑aware clients can declare the server in their configuration. The assistant then invokes the generated tools as if they were native functions, automatically handling parameter validation and response parsing based on the OpenAPI schema. This seamless coupling reduces friction for developers, accelerates feature delivery, and ensures that the AI’s interactions remain consistent with the underlying API contracts.