A few months ago, I had a simple question: why is it so hard for AI agents to get reliable, structured product prices? LLMs can reason about almost anything — but ask one "what's the cheapest Raspberry Pi 5 right now?" and it either hallucinates a number or admits it doesn't know. The data exists on marketplaces. The problem is the bridge between that data and the agent. So I built AgentShare — a price infrastructure API designed specifically for AI agents, not human users. Most price comparison tools are built for browsers: ads, affiliate banners, SEO-optimized pages. An agent doesn't need any of that. It needs: Structured JSON, not HTML to parse Freshness signals, not just a number Predictable error shapes it can act on A stable interface it can call repeatedly AgentShare is built around those four things. curl -H "X-API-Key: YOUR_KEY" \ "https://agentshare.dev/api/v1/offers/best?q=nvidia+jetson+orin" { "status": "ok", "data": { "best_offer": { "price": 4875000, "source": "tiki", "availability": true, "crawled_at": "2026-04-15T02:00:20Z", "data_age_seconds": 86400, "freshness_status": "stale" }, "summary": "Best price at Tiki — 4,875,000₫" }, "meta": { "remaining_requests": 99, "freshness_status": "stale" } } Every response includes freshness_status — fresh, stale, or expired — so the agent can decide whether to caveat its answer or ask for a refresh. If you're building with Claude Desktop, Cursor, or any MCP-compatible client, you can connect AgentShare as a tool server in one step: { "mcpServers": { "agentshare": { "command": "npx", "args": [ "-y", "mcp-remote", "https://agentshare.dev/mcp", "--header", "X-API-Key:YOUR_KEY" ] } } } Tools exposed: price_search, best_offer, best_offer_under_budget, service_meta. Each tool returns two content blocks: a one-line human-readable summary and a full JSON payload — so the agent has both a quick answer and machine-parseable data in the same response. A price from 6 days ago is not the same as a price from 2 hours ago. An agent recommending a product without knowing data age is doing the user a disservice. Every response includes crawled_at, data_age_seconds, and freshness_status. The agent can use this to decide whether to add a caveat, trigger a refresh, or tell the user "this price is a few days old." Early on, a missing product returned a generic NOT_FOUND error. An agent receiving that has no idea what to do next. Now the API distinguishes: // Product is outside current coverage { "status": "no_data", "reason": "out_of_coverage", "coverage_url": "https://agentshare.dev/coverage", "suggestions": ["similar product A", "similar product B"] } // Product is in coverage but not yet crawled { "status": "no_data", "reason": "pending_crawl", "estimated_available": "2026-04-22T00:00:00Z" } out_of_coverage → the agent redirects or tells the user clearly. pending_crawl → the agent knows to try again later. FastMCP handler calling the REST API with requests.get() — synchronously — inside an async handler on a single Uvicorn worker. The tool call would hang for exactly 120 seconds before failing. Root cause: the synchronous HTTP call blocked the event loop, which meant the inbound REST request never got processed, which meant the MCP tool call waited forever. Fix: move all API calls inside MCP tools to asyncio.to_thread() so requests runs on a thread pool, freeing the event loop. If you're building MCP servers with FastMCP on a single worker, watch out for this pattern. I'm honest about what's covered well versus what's still being built. High quality, updated regularly: AI edge hardware: Raspberry Pi, NVIDIA Jetson, Orange Pi, Google Coral, Rockchip SBCs Mini PCs: Intel NUC, Beelink, GMKtec Components: SSD, RAM, power supplies, cooling Robotics: kits, servo motors, arms, LiPo/LiFePO4 battery packs Expanding (beta — data may be incomplete): Mobile phones, tablets, general consumer electronics The focus list reflects what matters most to people building AI agents locally — the folks running LLMs on edge hardware or sourcing parts for robot projects. Full coverage spec: agentshare.dev/coverage Response shape — is the current JSON easy to work with in your agent framework, or are there fields you'd add/remove? Freshness thresholds — how old is "too old" for a price recommendation in your use case? Coverage gaps — what hardware or component categories are you looking for that aren't covered yet? Docs: agentshare.dev/docs Free tier: 100 requests/month, no credit card required OpenAPI spec: agentshare.dev/openapi.json Agent discovery: agentshare.dev/agent.json This is a solo project. I'm not a professional developer — I learned by building, with a lot of help from AI tools. If the approach is useful for what you're building, or if something is broken or confusing, I'd genuinely like to know.