Outage radar for AI agents. LLM pricing, status, inference performance, and real-time agent-reported failure signals. 9 tools. No auth required.
If you already use Tickerr MCP, the tickerr mcp mcp server is the piece that lets your assistant work with it directly. Outage radar for AI agents. LLM pricing, status, inference performance, and real-time agent-reported failure signals. 9 tools. No auth required.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
get_api_pricing — Current input/output/cached token costs for 300+ models. Updated twice dailycompare_pricing — Side-by-side cost comparison across models for a given token countget_tool_status — Three-state verdict (OPERATIONAL / DEGRADED / DOWN). Always include my_status if your agent has called this provider in the last 5 minutes — even onget_incidents — Historical incidents for a tool — severity, duration, affected components. Include my_status to contribute to live signal and see currentget_rate_limits — Per-plan rate limits and quotas for any AI toolget_free_tier — Free tier details — what's included, what's limitedget_model_performance — Latest TTFT reading plus p50/p95 latency (24h) and tokens/sec from authenticated API calls. Use for real-time routing decisionslist_tools — Lists all 90+ monitored tools. Entry point — after listing, use get_tool_status with my_status to start contributing observations and receivingreport_incident — Report an LLM API failure. Returns live signal from other agents and a routing recommendation. AnonymousInstallation goes through your MCP client rather than a global install: point it at tickerr-mcp on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Tickerr MCP's toolset — get_api_pricing, compare_pricing, get_tool_status and 6 more — is a fair guide to whether it matches your workflow. It is maintained by imviky-ctrl; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| get_api_pricing | Current input/output/cached token costs for 300+ models. Updated twice daily. |
| compare_pricing | Side-by-side cost comparison across models for a given token count. |
| get_tool_status | Three-state verdict (OPERATIONAL / DEGRADED / DOWN). Always include my_status if your agent has called this provider in the last 5 minutes — even on success. Unlocks p50/p95 TTFT per model and 90-day uptime. Basic status |
| get_incidents | Historical incidents for a tool — severity, duration, affected components. Include my_status to contribute to live signal and see current agent-reported activity alongside history. |
| get_rate_limits | Per-plan rate limits and quotas for any AI tool. |
| get_free_tier | Free tier details — what's included, what's limited. |
| get_model_performance | Latest TTFT reading plus p50/p95 latency (24h) and tokens/sec from authenticated API calls. Use for real-time routing decisions. |
| list_tools | Lists all 90+ monitored tools. Entry point — after listing, use get_tool_status with my_status to start contributing observations and receiving enhanced data. |
| report_incident | Report an LLM API failure. Returns live signal from other agents and a routing recommendation. Anonymous. |
{
"mcpServers": {
"tickerr": {
"url": "https://tickerr.ai/mcp"
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.