YepCode MCP Server

Overview

YepCode MCP Server bridges the gap between AI assistants and the YepCode cloud platform, enabling seamless execution of LLM‑generated scripts within secure, production‑ready environments. By exposing YepCode’s rich process orchestration capabilities through the Model Context Protocol, the server allows any AI client that supports MCP to invoke complex workflows—such as CI/CD pipelines, data transformations, or automated deployments—without leaving the conversational interface. This eliminates the need for custom integrations and keeps developers focused on higher‑level problem solving.

The server’s core value lies in its zero‑configuration conversion of YepCode processes into AI‑ready tools. Once authenticated, an assistant can request the execution of a specific process by name or ID, pass parameters, and receive real‑time status updates via Server‑Sent Events. This tight coupling gives developers the ability to trigger entire pipelines, monitor progress, and retrieve results—all within a single chat or command line session. The isolated execution environment guarantees that code runs safely, protecting sensitive data and complying with enterprise security policies.

Key capabilities include:

• Process Invocation: Call any YepCode workflow directly from the assistant, passing arguments as structured JSON.
• Real‑time Feedback: Stream logs and status updates through SSE, allowing users to see execution progress live.
• Secure Execution: All runs happen in YepCode’s sandboxed containers, ensuring isolation and compliance.
• Cross‑Platform Compatibility: The server can be deployed locally (via NPX or Docker) or accessed through a hosted endpoint, fitting into existing DevOps pipelines.

Typical use cases involve automating repetitive tasks such as code linting, test execution, or deployment triggers. A developer can ask the assistant to “run integration tests on branch X,” and the server will launch the appropriate YepCode process, stream logs back to the user, and report success or failure. In a data engineering context, an analyst might ask for “transform dataset Y using pipeline Z,” and the assistant will orchestrate the entire ETL flow, returning the transformed data or a download link.

Integration is straightforward: an MCP client only needs to register the YepCode server URL and provide the API token. Once connected, the assistant can discover available tools, prompt for parameters, and execute workflows as if they were native commands. This tight integration turns the AI assistant into a powerful, secure command‑line interface for YepCode, empowering teams to accelerate delivery while maintaining control over production environments.