Which integrations are available for this server?

Provides a secure workspace for executing Python scripts and managing files, enabling AI agents to perform data processing, code execution, and automation in a sandboxed environment.

How do I use AI Workspace MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AI Workspace MCP Server create a python script to calculate Fibonacci numbers and run it" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

AI Workspace MCP Server

A Model Context Protocol (MCP) server that provides AI with a secure workspace for file management and Python script execution. Designed to run on Vercel as a serverless function.

Features

File Management Tools

create_file - Create new files with content
read_file - Read file contents
update_file - Update existing files
delete_file - Delete files
list_files - List files and directories
create_directory - Create new directories

Code Execution

execute_python - Execute Python scripts with arguments (30-second timeout)

Setup on Vercel

1. Install Vercel CLI (Optional)

npm install -g vercel

2. Project Structure

Your project should look like this:

ai-workspace-mcp/ ├── api/ │ └── mcp.py # Serverless function ├── vercel.json # Vercel configuration ├── requirements.txt # Python dependencies └── README.md # This file

3. Deploy to Vercel

Option A: Deploy via Vercel Dashboard

Go to vercel.com
Click "Add New" → "Project"
Import your Git repository (or upload files)
Vercel will auto-detect Python and deploy

Option B: Deploy via CLI

# Login to Vercel vercel login # Deploy vercel # Deploy to production vercel --prod

4. Get Your Deployment URL

After deployment, Vercel will give you a URL like: https://your-project-name.vercel.app

API Endpoints

Once deployed, your server will have these endpoints:

GET /

Returns server information and status

curl https://your-project.vercel.app/

GET /health

Health check endpoint

curl https://your-project.vercel.app/health

GET /tools

List all available tools

curl https://your-project.vercel.app/tools

POST /execute

Execute a tool

curl -X POST https://your-project.vercel.app/execute \ -H "Content-Type: application/json" \ -d '{ "tool": "create_file", "arguments": { "filepath": "hello.py", "content": "print(\"Hello World!\")" } }'

Using with AI Clients

Claude Desktop Configuration

Add this to your Claude Desktop config:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

{ "mcpServers": { "ai-workspace": { "command": "curl", "args": [ "-X", "POST", "https://your-project.vercel.app/execute", "-H", "Content-Type: application/json", "-d", "@-" ] } } }

Using the API Directly

You can integrate this with any AI that supports HTTP tool calling:

import requests # Create a file response = requests.post( "https://your-project.vercel.app/execute", json={ "tool": "create_file", "arguments": { "filepath": "script.py", "content": "print('Hello from AI!')" } } ) print(response.json()) # Execute the file response = requests.post( "https://your-project.vercel.app/execute", json={ "tool": "execute_python", "arguments": { "filepath": "script.py" } } ) print(response.json())

Security Features

Sandboxed Workspace: All file operations are restricted to /tmp/workspace
Path Validation: Prevents directory traversal attacks
Execution Timeout: Python scripts are limited to 30 seconds
CORS Enabled: Allows cross-origin requests
Serverless Isolation: Each request runs in an isolated environment

Tool Examples

Create and Execute a Python Script

# Create a file curl -X POST https://your-project.vercel.app/execute \ -H "Content-Type: application/json" \ -d '{ "tool": "create_file", "arguments": { "filepath": "hello.py", "content": "print(\"Hello from Vercel!\")" } }' # Execute it curl -X POST https://your-project.vercel.app/execute \ -H "Content-Type: application/json" \ -d '{ "tool": "execute_python", "arguments": { "filepath": "hello.py" } }'

List Files

curl -X POST https://your-project.vercel.app/execute \ -H "Content-Type: application/json" \ -d '{ "tool": "list_files", "arguments": {} }'

Create Directory Structure

curl -X POST https://your-project.vercel.app/execute \ -H "Content-Type: application/json" \ -d '{ "tool": "create_directory", "arguments": { "dirpath": "scripts" } }'

Response Format

All tool executions return JSON:

Success Response:

{ "success": true, "message": "Successfully created file: hello.py\nSize: 26 bytes" }

Error Response:

{ "success": false, "error": "File not found: nonexistent.py" }

Execute Python Response:

{ "success": true, "exit_code": 0, "stdout": "Hello from Vercel!\n", "stderr": "" }

Important Notes

Vercel Limitations

Temporary Storage: Files in /tmp are ephemeral and cleared between invocations
10-second timeout: Vercel functions timeout after 10 seconds on free tier (25s on Pro)
Cold Starts: First request may be slower due to cold start
No Persistent State: Each function invocation starts fresh

For Persistent Storage

If you need persistent file storage, consider:

Using Vercel KV, Postgres, or Blob storage
Integrating with AWS S3, Google Cloud Storage, etc.
Using a database to store file contents

Environment Variables (Optional)

You can set environment variables in Vercel Dashboard:

WORKSPACE_PATH - Custom workspace path (default: /tmp/workspace)
EXECUTION_TIMEOUT - Python execution timeout in seconds (default: 30)

Local Development

Test locally before deploying:

# Install dependencies pip install -r requirements.txt # Run with Python's built-in server cd api python -m http.server 8000 # Or use Vercel CLI vercel dev

Then test with:

curl http://localhost:8000/health

Troubleshooting

"Module not found" errors

Ensure requirements.txt is in the project root and contains all dependencies.

Timeout errors

Reduce Python script complexity
Upgrade to Vercel Pro for longer timeouts
Use async operations where possible

File not persisting

Remember: /tmp storage is ephemeral on Vercel. Files won't persist between invocations.

Advanced Usage

Custom MCP Client

class VercelMCPClient: def __init__(self, base_url): self.base_url = base_url def call_tool(self, tool_name, arguments): response = requests.post( f"{self.base_url}/execute", json={"tool": tool_name, "arguments": arguments} ) return response.json() def list_tools(self): response = requests.get(f"{self.base_url}/tools") return response.json() # Usage client = VercelMCPClient("https://your-project.vercel.app") result = client.call_tool("create_file", { "filepath": "test.py", "content": "print('test')" })

Contributing

Feel free to extend this server with additional tools:

Add tool definition to get_tools()
Implement handler in execute_tool()
Update documentation

License

MIT License - modify and use as needed.

Support

Vercel Docs: vercel.com/docs
MCP Protocol: modelcontextprotocol.io
Issues: Open an issue or modify the code as needed