Tickerr is a monitoring platform for AI APIs that agents discover and use via MCP for pricing and status checks. The author asks the community to integrate its incident reporting to improve real-time outage detection.
Been building Tickerr for the past few months. Started as an independent monitoring platform for AI APIs but something interesting happened when we launched an MCP server. Agents started calling it on their own. because it showed up in MCP registries and agents found it. We now see around 1000 MCP requests per day, mostly agents checking pricing and status before deciding which model to call. The two most called tools are **compare\_pricing** and **get\_model\_performance**. Agents are using Tickerr to figure out which model is cheapest and actually responding right now, then routing accordingly. That was not the original plan but it makes complete sense. Here is what Tickerr has right now that is useful for agents: * Live status for 50+ AI services checked every 5 minutes via independent probes * API pricing for 300+ models updated twice daily * 2500+ incidents logged across 96 tools going back months * Model performance (p50/p95 latency from real API calls) The data gap we keep seeing: official status pages lag real outages by 15 to 30 minutes. Gemini has been a good example of this, their status page shows green while our probes show degraded. Agents hitting that during a workflow have no way to know unless something like Tickerr is in the loop. **Community ask:** If you are building AI agents that call LLM APIs, it would genuinely help the whole community if you looped Tickerr into your error handling. When your agent hits a failure, report it back. The more agents reporting, the faster everyone else finds out when something is actually broken. The MCP server has a **report\_incident** tool. When your agent gets a *529* or a *timeout* or anything unexpected, one call to **report\_incident** sends the signal. Other agents querying Tickerr then see it in near real time. Think of it as Waze but for LLM APIs. Your agent reports what it sees, every other agent benefits. Right now we have a cold start problem where not enough agents are reporting. If a few people here add the report hook to their error handlers it would make a real difference. Happy to answer any questions on how the data works or how to integrate it.
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