What is YepCode MCP Server?

An MCP (Model Context Protocol) server that enables AI platforms to interact with YepCode's infrastructure. Run LLM generated scripts and turn your YepCode processes into powerful tools that AI assistants can use directly.

Why YepCode MCP Server?

• Seamless AI Integration: Convert YepCode processes into AI-ready tools with zero configuration
• Real-time Process Control: Enable direct interaction between AI systems and your workflows
• Enterprise-Grade Security: Execute code in YepCode's isolated, production-ready environments
• Universal Compatibility: Integrate with any AI platform supporting the Model Context Protocol

Integration Guide

YepCode MCP server can be integrated with AI platforms like Cursor or Claude Desktop using either a remote approach (we offer a hosted version of the MCP server) or a local approach (NPX or Docker installation is required).

Remote Approach using SSE Server

1. Sign up to YepCode Cloud
2. Get your MCP Server URL from your workspace under: Settings > API credentials.
3. Add the following configuration to your AI platform settings:

{
  "mcpServers": {
    "yepcode-mcp-server": {
      "url": "https://cloud.yepcode.io/mcp/sk-c2E....RD/sse"
    }
  }
}

Local Approach

Required Environment Variables

• YEPCODE_API_TOKEN: Your YepCode API token. How to obtain:
  1. Sign up to YepCode Cloud
  2. Get your API token from your workspace under: Settings > API credentials

Using NPX

Add the following configuration to your AI platform settings:

{
  "mcpServers": {
    "yepcode-mcp-server": {
      "command": "npx",
      "args": ["-y", "@yepcode/mcp-server"],
      "env": {
        "YEPCODE_API_TOKEN": "your_api_token_here",
      }
    }
  }
}

Using Docker

1. Build the container image:

docker build -t yepcode/mcp-server .

2. Add the following configuration to your AI platform settings:

{
  "mcpServers": {
    "yepcode-mcp-server": {
      "command": "docker",
      "args": [
        "run",
        "-d",
        "-e",
        "YEPCODE_API_TOKEN=your_api_token_here",
        "yepcode/mcp-server"
      ]
    }
  }
}

Debugging

Debugging MCP servers can be tricky since they communicate over stdio. To make this easier, we recommend using the MCP Inspector, which you can run with the following command:

npm run inspector

This will start a server where you can access debugging tools directly in your browser.

YepCode MCP Tools Reference

The MCP server provides several tools to interact with YepCode's infrastructure:

Code Execution

run_code

Executes code in YepCode's secure environment.

// Input
{
  code: string;                          // The code to execute
  options?: {
    language?: string;                   // Programming language (default: 'javascript')
    comment?: string;                    // Execution context
    settings?: Record<string, unknown>;  // Runtime settings
  }
}

// Response
{
  returnValue?: unknown;                 // Execution result
  logs?: string[];                       // Console output
  error?: string;                        // Error message if execution failed
}

Environment Management

set_env_var

Sets an environment variable in the YepCode workspace.

// Input
{
  key: string;                           // Variable name
  value: string;                         // Variable value
  isSensitive?: boolean;                 // Whether to mask the value in logs (default: true)
}

remove_env_var

Removes an environment variable from the YepCode workspace.

// Input
{
  key: string;                           // Name of the variable to remove
}

Process Execution

The MCP server can expose your YepCode Processes as individual MCP tools, making them directly accessible to AI assistants. This feature is enabled by just adding the mcp-tool tag to your process (see our docs to learn more about process tags).

There will be a tool for each exposed process: run_ycp_<process_slug> (or run_ycp_<process_id> if tool name is longer than 60 characters).

run_ycp_<process_slug>

// Input
{
  parameters?: any;                      // This should match the input parameters specified in the process
  options?: {
    tag?: string;                        // Process version to execute
    comment?: string;                    // Execution context
  };
  synchronousExecution?: boolean;        // Whether to wait for completion (default: true)
}

// Response (synchronous execution)
{
  executionId: string;                   // Unique execution identifier
  logs: string[];                        // Process execution logs
  returnValue?: unknown;                 // Process output
  error?: string;                        // Error message if execution failed
}

// Response (asynchronous execution)
{
  executionId: string;                   // Unique execution identifier
}

get_execution

Retrieves the result of a process execution.

// Input
{
  executionId: string;                   // ID of the execution to retrieve
}

// Response
{
  executionId: string;                   // Unique execution identifier
  logs: string[];                        // Process execution logs
  returnValue?: unknown;                 // Process output
  error?: string;                        // Error message if execution failed
}

License

This project is licensed under the MIT License - see the LICENSE file for details.