CMMV MCP Server

The Cmmv Mcp server implements the Model Context Protocol (MCP) within the CMMV framework, turning a conventional Node.js application into a fully‑featured AI assistant backend. It solves the common pain point of wiring large language models (LLMs) to business logic: developers no longer need to write custom adapters or manually marshal JSON payloads. Instead, MCP provides a declarative contract‑based interface that automatically exposes application services as tools the LLM can call, ensuring type safety and consistent error handling across all integrations.

At its core, the server offers a bidirectional communication channel between any LLM client and the CMMV application. The protocol is transport‑agnostic, supporting HTTP streams, Server‑Sent Events (SSE), or even standard I/O for local testing. This flexibility lets teams embed the MCP server into existing HTTP stacks or run it as a standalone microservice without touching their core business logic. The decorator turns ordinary functions into LLM‑friendly tools, while Zod schemas validate inputs at runtime, preventing malformed requests from reaching the application layer.

Key capabilities include:

• Tool registration and discovery – The endpoint lists all available tools, making it trivial for an LLM to introspect the service surface.
• Session management – Multiple concurrent MCP sessions are supported; each session is identified by a unique ID, allowing parallel conversations or workflows without interference.
• Health and diagnostics – A endpoint provides instant uptime checks, while CORS and DNS rebinding protections safeguard the server in production environments.
• Hook integration – CMMV’s hook system can intercept requests, augment authentication, or log usage metrics seamlessly.
• TypeScript friendliness – Full type definitions enable compile‑time safety, reducing runtime errors and speeding up development cycles.

In practice, the server shines in scenarios such as chat‑based business assistants, automated data pipelines, or interactive APIs. For example, a sales team can expose a tool that the LLM calls after parsing customer emails, while a data science team can expose a tool that triggers complex analytics workflows. The declarative nature of MCP means new tools can be added with a single decorator, and the LLM automatically learns how to use them without additional training data.

By abstracting away transport details, validation logic, and session handling, Cmmv Mcp lets developers focus on crafting meaningful application contracts. The result is a robust, secure, and scalable bridge between LLMs and enterprise services—exactly what modern AI‑powered workflows demand.