YepCode

The YepCode MCP server acts as a powerful bridge that gives AI assistants the ability to execute the code they generate in a safe and secure environment. Instead of just writing scripts for a user to copy and paste, an AI can now run those snippets directly within an isolated sandbox. This transforms a standard chat interface into an active workstation where the AI can perform calculations, process data, and automate repetitive tasks on behalf of the developer. Moving deeper into its capabilities, this tool supports both JavaScript and Python, providing full access to the massive libraries available through NPM and PyPI. It offers more than just simple execution; it includes a complete suite of management tools for file storage and environment variables. This means an AI can upload files, list storage contents, and maintain persistent settings across different sessions, all while operating within YepCode’s enterprise-grade infrastructure. For developers looking for more sophisticated automation, the server can seamlessly turn existing YepCode processes into specialized AI tools with a simple tagging system. It supports flexible integration methods, allowing users to connect via a hosted remote server (SSE) or a local setup using Docker and NPX. By providing real-time logs and detailed execution feedback, it enables LLMs to debug their own work and interact with complex workflows in a way that is both scalable and highly secure.

Category: Cloud & Infrastructure

Tags: automation, execution, javascript, python, sandbox

Visit YepCode

How to install and configure YepCode

1. Installation To use the YepCode MCP server, you first need to obtain your API credentials: 1. Sign up for a YepCode Cloud account. 2. Navigate to Settings > API credentials to create a new API token. For Local Installation, you have two options: * NPX: Requires Node.js version 18 or higher. No manual install is needed as it can be run via npx. * Docker: bash docker build -t yepcode/mcp-server . ---

2. Configuration Depending on your client (Claude Desktop, Cursor, etc.) and preferred connection method, add one of the following blocks to your mcpServers configuration file:

Remote Approach (Hosted SSE) If your client does not support authentication headers: json { "mcpServers": { "yepcode-mcp-server": { "url": "https://cloud.yepcode.io/mcp/<YOUR_API_TOKEN>/sse" } } } If your client supports authentication headers: json { "mcpServers": { "yepcode-mcp-server": { "url": "https://cloud.yepcode.io/mcp/sse", "headers": { "Authorization": "Bearer <YOUR_API_TOKEN>" } } } }

Local Approach (NPX) json { "mcpServers": { "yepcode-mcp-server": { "command": "npx", "args": ["-y", "@yepcode/mcp-server"], "env": { "YEPCODE_API_TOKEN": "your_api_token_here" } } } }

Local Approach (Docker) json { "mcpServers": { "yepcode-mcp-server": { "command": "docker", "args": [ "run", "-i", "-e", "YEPCODE_API_TOKEN=your_api_token_here", "yepcode/mcp-server" ] } } } Note: You can also pass YEPCODE_MCP_OPTIONS in the env block (for local) or as a query parameter ?mcpOptions=... (for remote) to disable certain tools like disableRunCodeTool. ---

3. Available Tools * run_code: Executes LLM-generated JavaScript or Python code in YepCode's secure sandbox. * set_env_var: Sets an environment variable in the YepCode workspace. * remove_env_var: Removes an environment variable from the workspace. * list_files: Lists all files stored in the YepCode system. * upload_file: Uploads a file (text or base64 binary) to YepCode storage. * download_file: Downloads a file from YepCode storage. * delete_file: Deletes a specific file from storage. * run_ycp_<process_slug>: Runs a specific YepCode process that has been tagged with mcp-tool. * get_execution: Retrieves the status, logs, and results of a specific process execution. ---

4. Example Prompts The provided content does not list specific natural language prompts, but usage involves asking the AI to perform tasks such as: * "Run this Python script in the YepCode sandbox to process this CSV data." * "Upload the current analysis results to my YepCode storage as 'report.txt'." * "Execute my YepCode process named 'sync-database-records'." * "Set an environment variable named 'API_KEY' in my YepCode workspace."

What you can do with YepCode

Use Case 1: On-Demand Data Transformation and Reporting Problem: Users often have large datasets (like CSV or JSON files) that require complex manipulation, such as pivot tables, statistical analysis, or cleaning, which an LLM cannot perform reliably on its own due to context limits or calculation errors. Solution: The YepCode MCP allows the AI to generate a specialized Python (Pandas) or JavaScript script and execute it immediately in a secure sandbox. The AI can use the run_code tool to process the data and return the final summary or a processed file. Example: A user uploads a 50MB sales CSV to YepCode storage. They ask Claude to "Calculate the month-over-month growth for each product category." Claude generates a Python script using Pandas, calls run_code, and provides the user with the specific growth percentages calculated in the sandbox.

Use Case 2: Bridging AI to Internal Business Processes Problem: AI assistants usually lack the ability to trigger internal company workflows (like onboarding a user, generating an invoice, or clearing a cache) because these systems are behind private APIs or require specific business logic. Solution: Developers can create these workflows as "Processes" in YepCode and add the mcp-tool tag. This automatically exposes the workflow as a custom tool (e.g., run_ycp_onboard_employee) to the AI assistant. Example: An HR manager tells Claude Desktop, "Onboard new employee John Smith as a Developer in the Madrid office." Claude identifies the run_ycp_onboard_employee tool, maps the parameters (name, role, location), and triggers the YepCode process to create accounts in Slack, Jira, and the company HRIS.

Use Case 3: Rapid API Orchestration using NPM

PyPI Packages Problem: Writing a script to integrate three different APIs (e.g., GitHub, Slack, and Notion) requires a developer to set up a local environment, manage dependencies, and handle authentication. Solution: The YepCode MCP provides a secure environment with full support for NPM and PyPI. An AI can generate and run a script that imports professional libraries (like axios, octokit, or notion-sdk-js) to orchestrate data movement between services without any local setup. Example: A developer asks Cursor, "Get all my open GitHub issues labeled 'bug' and create a summary list in a new Notion page." The AI writes a script using the GitHub and Notion NPM packages, executes it via run_code, and confirms the task is complete.

Use Case 4: Automated File Management and Document Conversion Problem: Users often need to convert files between…

Key facts

• Freemium
• Cloud & Infrastructure, Developer Tools & Code Intelligence, Files, Documents & PDFs
• automation, execution, javascript, python, sandbox

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