`get_tool_status` and `get_incidents` answer the question you have when calls start failing — is it them or is it me — with an uptime figure and 90 days of history instead of a guess, while `compare_pricing` turns per-1M-token rates into a ranked cost for the token counts you actually send. `report_incident` runs the opposite direction and its own description asks to be called automatically before retrying any 5xx or timeout, which is worth deciding on purpose rather than by default, because every such call ships your provider, model, error code and latency off the machine.
A status and pricing feed for AI tools and model APIs — 8 tools over the 90+ services tickerr.ai monitors — covering live operational status, 90 days of incident history, per-token pricing, rate limits and free-tier plans, plus a call that reports a failure your own agent has just hit.
- Live status with the checking interval on the record: `get_tool_status` returns operational status, uptime percentage and response time for a `slug`, checked every 5 minutes from independent infrastructure.
- History that separates a blip from a pattern: `get_incidents` returns outages and degradations from the last 90 days for a `slug`, drawn from official provider status pages alongside Tickerr's own monitoring.
- The monitored set itself: `list_tools` enumerates the 90+ AI tools tracked, ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Perplexity, DeepSeek, Groq and Fireworks AI among them.
- Pricing in the unit you are billed in: `get_api_pricing` returns current input and output cost per 1M tokens, narrowed by a model or provider `filter`.
- Cost ranked against a workload you actually run: `compare_pricing` ranks models by total cost for the input and output token counts you give it, with `top` and `filter` to trim the field.
- The limits that decide whether a plan fits: `get_rate_limits` returns requests per minute, tokens per day and context window by plan tier for a `slug`.
- Free plans gathered by what they are for: `get_free_tier` groups the best free plans by `category` — LLM APIs, coding assistants, image generation.
- A path in the reverse direction: `report_incident` submits a failure your agent just experienced — provider, model, error code and latency, no request content — and answers with how many other agents are reporting the same thing, whether the signal is confirmed, and which model to fall back to.
No account and no key. What to know before wiring it in is the direction of `report_incident`: it sends your provider, model, error code and latency to tickerr.ai, so it writes into someone else's dataset rather than reading from it. Reporting stays active for seven days after your last call and renews on the next one.
One command — npx -y tickerr-mcp