FlowMCP Core – A Unified API‑to‑MCP Translator

FlowMCP Core addresses a common pain point for developers building AI assistants: the need to expose heterogeneous web services as clean, type‑safe, and testable tools. Traditional REST APIs are often undocumented or inconsistently structured, making it difficult for an AI assistant to discover and call them reliably. FlowMCP Core turns any REST endpoint into a Model Context Protocol (MCP) interface with minimal effort, providing a single source of truth for the assistant to consume. This eliminates the need for custom wrappers or manual schema definitions, streamlining integration across diverse services.

At its core, FlowMCP Core takes a declarative schema that describes an API’s namespace, base URL, headers, tags, and individual routes. From this schema it automatically generates a fully‑functional MCP tool that handles request construction, parameter substitution, and error handling. The generated tool is immediately available for activation on an MCP server, allowing the assistant to call the API with a simple, consistent syntax. Because the schema is validated against a robust Zod‑based type system, developers receive instant feedback on missing or malformed definitions before deployment.

Key capabilities include:

• Universal API Adaptation – Convert any REST endpoint into an MCP tool with a single schema file.
• Schema‑Driven Validation – Multi‑layer checks for schemas, parameters, and responses ensure that only well‑formed tools reach production.
• Advanced Filtering – Namespace, tag, and route filtering with case‑insensitive matching lets developers expose only the tools relevant to a given assistant.
• Built‑in Testing Framework – Automatic test generation and execution for every route guarantees that the tool behaves as expected.
• Native HTTP Client – A fetch‑based client with intelligent error handling reduces boilerplate and improves reliability.
• Zod Integration – Automatic TypeScript/Zod interface generation provides compile‑time safety for developers working in typed environments.

Typical use cases span from finance and e‑commerce to IoT. For example, a fintech assistant can expose market data APIs as MCP tools without writing custom adapters; an e‑commerce bot can query inventory and pricing services through a single, validated interface. In each scenario, FlowMCP Core removes the overhead of manual integration and testing, allowing developers to focus on business logic rather than plumbing.

Integration into AI workflows is seamless. Once a tool is activated, the assistant can invoke it through standard MCP calls, receiving structured responses that can be directly consumed or transformed. The framework’s command‑line interface and comprehensive documentation make it easy to scaffold, validate, and deploy new tools, while its performance optimizations keep latency low even under heavy load. Overall, FlowMCP Core provides a powerful, developer‑friendly bridge between legacy REST services and modern AI assistants.