Mcp Code Runner

What does MCP Code Runner do?

MCP Code Runner executes JavaScript and Python code snippets in a sandboxed environment via the Model Context Protocol (MCP), returning output, errors, and execution timing for AI agent computation tasks. Visit Node.js to see the type of data this actor works with. The actor outputs clean, structured JSON data ready for analysis, export, and integration with your AI workflows.

Why use MCP Code Runner?

• MCP compatible � works with AI agents via the Model Context Protocol for seamless tool integration
• Structured output � get clean JSON ready for analysis via the Apify API
• Proxy support � leverage Apify Proxy for reliable data collection without IP blocks
• Scalable � handle large volumes with automatic retry and error handling
• No coding required � run directly from the Apify Console or integrate via REST API
• Flexible export � download results in JSON, CSV, Excel, or push to databases and APIs

How to use MCP Code Runner

1. Navigate to the Apify Store and find MCP Code Runner
2. Click Try for free to open the actor configuration page in Apify Console
3. Configure the input parameters according to your needs
4. Click Start to run the actor and wait for completion
5. Preview results in the dataset tab, then download or export in your preferred format

Input configuration

Field	Type	Description	Default
code	string	Code to execute	console.log('Hello!')
language	string	javascript or python	javascript
timeout	integer	Max execution time (sec)	30

Output data

The actor outputs structured data for each result. Below is an example output record:

{
    "language": "javascript",
    "code": "console.log(2 + 2)",
    "status": "success",
    "output": "4",
    "errors": "",
    "executionTimeMs": 12
}

Cost of usage

MCP Code Runner uses pay-per-event pricing at $0.75 per 1,000 results ($0.00075 per result). Each actor start incurs a small $0.00005 charge. On the free Apify plan with $5/month in platform credits, you can process approximately 6,600 results per month.

Tips and advanced usage

• Set up scheduled runs using Apify Schedules for automated recurring data collection
• Use webhooks to get notified when runs complete and trigger downstream processing
• Combine with other MCP actors to build comprehensive AI agent tool suites on the Apify platform
• Adjust proxy settings for higher success rates against rate-limited or geo-restricted services
• Monitor usage via the Apify Console dashboard to track performance and optimize costs

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Built with Crawlee and Apify SDK. See more tools by consummate_mandala on Apify Store.