SpecBridge

SpecBridge is an MCP server that transforms any OpenAPI specification into a fully‑functional set of MCP tools with zero manual configuration. By simply dropping , , or files into a designated folder, developers can expose the entire REST surface of an API to AI assistants. This eliminates the need for hand‑crafted tool definitions, server wrappers, or complex deployment pipelines, allowing teams to iterate on API integrations quickly and safely.

The core value of SpecBridge lies in its filesystem‑first approach. The server watches a folder for spec files, parses them automatically, and generates tool names that mirror the API’s operations. When an OpenAPI spec contains an , SpecBridge uses it directly; otherwise, it constructs a clear, descriptive name from the HTTP method and path segments. This naming strategy keeps tools discoverable while preserving the semantics of the underlying API, making it intuitive for developers to reference and debug tool calls.

SpecBridge supports full OpenAPI features—including path parameters, request bodies, response schemas, and authentication. Authentication is handled by convention: environment variables named after the spec file (e.g., for ) are mapped to the appropriate header or bearer token automatically. This eliminates hard‑coded secrets and allows multiple APIs to coexist in the same server instance without namespace clashes. The built‑in debug command () lists all loaded specs and tools, giving instant visibility into what the server is exposing.

The server’s transport flexibility further enhances its integration potential. It can run over standard input/output for local experimentation or expose an HTTP streaming endpoint on a specified port, enabling remote AI assistants to query the tools over the network. Coupled with FastMCP’s lightweight runtime, SpecBridge can be launched as a single command or embedded within larger toolchains without additional infrastructure.

In real‑world scenarios, SpecBridge accelerates API onboarding for AI agents. For example, a data science team can expose the internal analytics API by simply adding its spec to the folder; an AI assistant can then call or similar tools without any code changes. Similarly, developers integrating third‑party services like GitHub, Stripe, or custom microservices can publish the corresponding specs and instantly provide AI‑powered automation capabilities. The zero‑configuration, auto‑authenticating nature of SpecBridge makes it an ideal bridge between formal API definitions and conversational AI workflows, delivering rapid, secure, and maintainable tool integration.