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BuyWhere MCP tackles AI agent price staleness with tiered crawling

BuyWhere has developed a three-tier system to keep product prices fresh for its shopping data server, BuyWhere MCP, which serves AI agents. Initially, attempts to crawl the entire product catalog frequently led to IP blocks. The current architecture prioritizes crawling 'hot' products (those in active agent conversations or price alerts) every 45 minutes, 'warm' products (in comparison pages) every 6 hours, and 'long-tail' products on a 3-day rolling window, re-crawling them upon access. This approach ensures that frequently accessed items are updated rapidly, while less popular items are refreshed less often, preventing stale data from being presented to AI agents like Claude and GPT. AI

IMPACT Provides a practical solution for maintaining real-time data freshness for AI agents, crucial for reliable product recommendations and comparisons.

RANK_REASON The item describes a technical implementation for a specific product, BuyWhere MCP, detailing its architecture and operational challenges.

Read on dev.to — MCP tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

BuyWhere MCP tackles AI agent price staleness with tiered crawling

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The item describes a technical implementation for a specific product, BuyWhere MCP, detailing its architecture and operational challenges.
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product, infra
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High
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35 days old
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COVERAGE [1]

  1. dev.to — MCP tag TIER_1 English(EN) · BuyWhere ·

    How We Keep MCP Server Prices Fresh Across 400M Products (Without Burning Down Our Database)

    <p>When we launched <a href="https://buywhere.ai/developers" rel="noopener noreferrer">BuyWhere MCP</a> — a shopping data server for AI agents — we thought the hard part was getting 296M products into a single endpoint.</p> <p>We were wrong.</p> <p>The hard part was keeping those…