What endpoints does Torobjo MCP Server provide?
Two core endpoints: `search_torob` for Torob.com product search and `get_instagram_caption` for Instagram caption extraction.
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基本情報
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The Torobjo MCP Server is a powerful implementation of the Model Context Protocol (MCP) for product search and Instagram analysis. It integrates with the Torob API for product searches and extracts captions from Instagram, featuring a modular design for easy use and optimized per
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概要
What is Torobjo MCP Server?
A production-grade MCP server that combines Torob.com product search for Persian-language products with Instagram content analysis. Built on FastMCP and designed for Claude Sonnet integration.
How to use Torobjo MCP Server?
Install and configure the server to expose its two core endpoints: search_torob for Torob product queries and get_instagram_caption for Instagram caption extraction. Use Claude or any MCP client to invoke these tools.
Key features of Torobjo MCP Server
- Full MCP protocol support with all core specifications
- Dual-mode operation: Torob search and Instagram processing
- Optimized for Claude Sonnet integration
- Automatic retry mechanisms and comprehensive error handling
- Rate limiting protection for enterprise reliability
- Modular tool design with unified JSON API
Use cases of Torobjo MCP Server
- Search Persian products on Torob.com with price filtering and pagination
- Extract Instagram captions from posts using headless browser automation
- Build Claude-powered assistants that query Iranian e-commerce data
- Automate social media content analysis alongside product lookup
よくある質問
Is the product search optimized for Persian language?
Yes, the Torob search endpoint is Persian-language optimized and supports pagination and price filtering.
What error handling mechanisms are in place?
The server includes automatic retry mechanisms, comprehensive error handling, and rate limiting protection for production reliability.
How does Instagram caption extraction work?
It uses headless browser automation with multiple fallback selectors to robustly extract captions, including error recovery.
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