PredictZ Bet Prediction Scraper
Deprecated
Pricing
$5.00/month + usage
PredictZ Bet Prediction Scraper
Deprecated
PredictZ Bet Prediction Scraper is a specialized tool designed to fetch betting predictions exclusively from PredictZ, one of the reputable betting prediction websites. This scraper's aim is to provide accurate and current betting predictions, focusing on the insights from PredictZ's platform.
0.0 (0)
Pricing
$5.00/month + usage
1
Total users
15
Monthly users
1
Last modified
2 years ago
You can access the PredictZ Bet Prediction 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.
$# Start Server-Sent Events (SSE) session and keep it running<curl "https://actors-mcp-server.apify.actor/sse?token=<YOUR_API_TOKEN>&actors=rikunk/predictz-scraper"
# Session id example output:# event: endpoint# data: /message?sessionId=9d820491-38d4-4c7d-bb6a-3b7dc542f1faUsing PredictZ Bet Prediction Scraper via Model Context Protocol (MCP) server
MCP server lets you use PredictZ Bet Prediction 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 PredictZ Bet Prediction Scraper Actor with the provided input.
$curl -X POST "https://actors-mcp-server.apify.actor/message?token=<YOUR_API_TOKEN>&session_id=<SESSION_ID>" -H "Content-Type: application/json" -d '{$ "jsonrpc": "2.0",$ "id": 1,$ "method": "tools/call",$ "params": {$ "arguments": {},$ "name": "rikunk/predictz-scraper"$ }$}'The response should be: Accepted. You should received response via SSE (JSON) as:
$event: message$data: {$ "result": {$ "content": [$ {$ "type": "text",$ "text": "ACTOR_RESPONSE"$ }$ ]$ }$}Configure local MCP Server via standard input/output for PredictZ Bet Prediction 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:
{ "mcpServers": { "actors-mcp-server": { "command": "npx", "args": [ "-y", "@apify/actors-mcp-server", "--actors", "rikunk/predictz-scraper" ], "env": { "APIFY_TOKEN": "<YOUR_API_TOKEN>" } } }}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.
