AppDog MCP Server

AppDog addresses the friction that often accompanies the integration of RESTful APIs into AI‑driven applications. By taking an OpenAPI specification as its single source of truth, AppDog automatically produces fully typed Python clients and a ready‑to‑run MCP (Model Context Protocol) server. This removes the need for manual client scaffolding, version drift, and repetitive configuration across projects, allowing developers to focus on business logic rather than boilerplate.

At its core, AppDog generates a Python package that mirrors the structure of an OpenAPI schema. Each endpoint becomes an async method with type‑annotated parameters and return values, while authentication details are injected via environment variables. The resulting client is immediately usable in both synchronous and asynchronous contexts, making it a drop‑in replacement for hand‑crafted wrappers. Beyond client generation, AppDog bundles a small CLI that lets teams add, remove, and lock API clients in a single repository. The lock file guarantees deterministic builds by recording exact spec hashes, ensuring that downstream consumers always hit the same API contract.

The standout feature is the automatic MCP server creation. With a single command, AppDog compiles all registered API clients into an MCP service that exposes each endpoint as a tool. The server can be deployed, run locally, or launched in development mode with an inspector for debugging. Because the server is generated from the same OpenAPI spec, it inherits all type safety and documentation, reducing runtime errors when AI assistants invoke external services. This tight coupling between specification, client, and MCP server eliminates the classic “contract mismatch” problem that plagues many API integrations.

In practice, AppDog shines in scenarios where AI assistants need to fetch data from multiple third‑party services. For example, a chatbot that aggregates product information from several e-commerce APIs can rely on AppDog to expose each catalog as a tool, while the assistant’s prompt engine selects the appropriate one based on user intent. Similarly, data‑driven pipelines that stitch together weather, financial, and social media APIs can use AppDog to guarantee consistent schemas across all stages. The generated MCP server also makes it trivial to expose internal services to external agents, enabling cross‑team automation without writing custom adapters.

Because AppDog’s output is pure Python and its MCP server uses FastMCP, developers can seamlessly embed the generated tools into existing frameworks or CI/CD workflows. The ability to lock API specifications, coupled with automatic client regeneration, ensures that AI agents always interact with the correct API version—an essential requirement for production‑grade AI services.