API Wrapper MCP Server

The API Wrapper MCP Server transforms any RESTful service into a first‑class MCP tool, allowing AI assistants such as Claude to invoke external APIs directly from within their conversation context. By abstracting the HTTP layer behind a simple, declarative configuration file, developers can expose third‑party endpoints to AI workflows without writing custom adapters or boilerplate code. This solves the common pain point of integrating diverse APIs into conversational agents—where each service typically requires its own SDK, authentication scheme, and request formatting logic.

At its core, the server reads a YAML configuration that describes each tool’s name, description, HTTP method (GET or POST), endpoint URL, and optional timeout. Parameters are defined with type information, required flags, defaults, and inline documentation, enabling the MCP client to present a clear, typed interface to users. The server automatically performs parameter validation and substitutes values into JSON bodies or query strings, supporting templated payloads with Jinja‑style placeholders. Authentication is handled through environment variables, so tokens or API keys never appear in the configuration file; they can also be referenced directly inside templates using a dedicated syntax. This design keeps sensitive data out of source control while still allowing the server to inject it at runtime.

Key capabilities include:

• Declarative API mapping: Define multiple endpoints in a single YAML file, each becoming an independent MCP tool.
• Type‑safe parameters: Enforce correct data types and provide defaults, reducing runtime errors.
• Custom timeouts: Fine‑tune request durations per tool to match API SLA requirements.
• Environment‑based auth: Securely inject tokens or keys without hardcoding them.
• Unified error handling: The server normalizes HTTP errors into MCP‑compatible responses, so the AI client can surface meaningful messages to users.

Real‑world scenarios where this server shines are plentiful. A data science team can expose a machine‑learning inference API as an MCP tool, letting analysts query predictions through Claude without writing Python wrappers. A customer‑support platform can expose ticket‑management endpoints, enabling agents to create or update tickets directly from a conversational interface. Any organization that maintains internal microservices can quickly surface them to AI assistants, accelerating prototyping and reducing duplication of effort across teams.

Integration with existing MCP workflows is seamless. After adding the server’s command and arguments to a Claude Desktop configuration, the assistant automatically discovers all declared tools. During a conversation, the user can invoke a tool by name; the assistant gathers required parameters from dialogue context, validates them against the server’s schema, and forwards a properly formatted HTTP request. The response is returned as structured data that the assistant can embed in its reply or pass to subsequent tools. This tight coupling preserves context continuity while extending the assistant’s capabilities beyond its native knowledge base.

In summary, the API Wrapper MCP Server offers a lightweight, secure, and highly configurable bridge between REST APIs and AI assistants. Its declarative approach eliminates repetitive boilerplate, enforces parameter correctness, and centralizes authentication management—making it an indispensable component for developers who want to enrich conversational agents with external services quickly and safely.