MCP API Server

Overview

The Mcp Server is a robust, enterprise‑grade API service designed to bridge conventional microservice endpoints with the Model Context Protocol (MCP). By exposing RESTful APIs as MCP tools, it eliminates the need for developers to write custom adapters or manually configure tool schemas. The server automatically ingests an OpenAPI specification, filters the exposed endpoints based on a configurable whitelist (), and converts each endpoint into an MCP tool with the appropriate name, description, and input schema. This dynamic discovery mechanism ensures that any change to the underlying API surface is reflected in the MCP toolset without additional code changes.

For developers building AI assistants, this server offers a seamless way to integrate existing business logic into conversational flows. An assistant can call any registered tool—such as “create user” or “update profile”—by simply passing the required arguments, and the server handles HTTP request construction, authentication, and response formatting. The result is a declarative, type‑safe interface that matches the assistant’s own schema expectations, reducing friction between AI logic and backend services.

Key capabilities include:

• Automatic tool registration from OpenAPI, eliminating manual configuration.
• Fine‑grained security filtering, exposing only whitelisted endpoints and safe parameters.
• Caching of OpenAPI data to improve performance and resilience against network hiccups.
• Unified error handling, ensuring that tool calls return consistent, informative responses to the AI client.
• TypeScript support throughout, providing compile‑time guarantees for both server and client developers.

Typical use cases span a wide range of scenarios. In an enterprise HR system, the server can expose user management APIs as MCP tools, enabling a chatbot to create or update employee records on demand. In e‑commerce, product catalog and order APIs become instantly available to a virtual assistant that can search inventory or place orders without custom glue code. Because the server operates over standard HTTP and follows OpenAPI, it can integrate with legacy systems, internal microservices, or third‑party APIs that already publish a spec.

Integrating the Mcp Server into an AI workflow is straightforward: configure the section of your MCP client with the server’s endpoint, credentials, and allowed APIs. The assistant then discovers available tools via the MCP ListTools request, automatically gaining access to all registered services. From there, each tool call translates into a secure HTTP request handled by the server, returning JSON payloads that the assistant can parse and present to users. This tight coupling between declarative tool definitions and backend logic gives developers a powerful, low‑maintenance pathway to enrich AI assistants with real‑world functionality.