
Chat GPT Code Interpreter Scraper
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Chat GPT Code Interpreter Scraper
Generate Python code using ChatGPT and run in a live Python environment. This a sandboxed Python environment where you can execute Python code to perform any task you like. Input a natural language prompt and ChatGPT will generate Python code to perform a task and the code will be executed in Apify.
You can access the Chat GPT Code Interpreter Scraper programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
1# Start Server-Sent Events (SSE) session and keep it running
2curl "https://actors-mcp-server.apify.actor/sse?token=<YOUR_API_TOKEN>&actors=datastorm/gpt-code-scraper"
3
4# Session id example output:
5# event: endpoint
6# data: /message?sessionId=9d820491-38d4-4c7d-bb6a-3b7dc542f1fa
Using Chat GPT Code Interpreter Scraper via Model Context Protocol (MCP) server
MCP server lets you use Chat GPT Code Interpreter Scraper within your AI workflows. Send API requests to trigger actions and receive real-time results. Take the received sessionId
and use it to communicate with the MCP server. The message starts the Chat GPT Code Interpreter Scraper Actor with the provided input.
1curl -X POST "https://actors-mcp-server.apify.actor/message?token=<YOUR_API_TOKEN>&session_id=<SESSION_ID>" -H "Content-Type: application/json" -d '{
2 "jsonrpc": "2.0",
3 "id": 1,
4 "method": "tools/call",
5 "params": {
6 "arguments": {
7 "prompt": "Plot a chart of BTC'\''s price for 2023 YTD",
8 "model": "gpt-3.5-turbo",
9 "datasetIds": [],
10 "files": []
11},
12 "name": "datastorm/gpt-code-scraper"
13 }
14}'
The response should be: Accepted
. You should received response via SSE (JSON) as:
1event: message
2data: {
3 "result": {
4 "content": [
5 {
6 "type": "text",
7 "text": "ACTOR_RESPONSE"
8 }
9 ]
10 }
11}
Configure local MCP Server via standard input/output for Chat GPT Code Interpreter Scraper
You can connect to the MCP Server using clients like ClaudeDesktop and LibreChat or build your own. The server can run both locally and remotely, giving you full flexibility. Set up the server in the client configuration as follows:
1{
2 "mcpServers": {
3 "actors-mcp-server": {
4 "command": "npx",
5 "args": [
6 "-y",
7 "@apify/actors-mcp-server",
8 "--actors",
9 "datastorm/gpt-code-scraper"
10 ],
11 "env": {
12 "APIFY_TOKEN": "<YOUR_API_TOKEN>"
13 }
14 }
15 }
16}
You can further access the MCP client through the Tester MCP Client, a chat user interface to interact with the server.
To get started, check out the documentation and example clients. If you are interested in learning more about our MCP server, check out our blog post.