About tickerr
Tickerr MCP gives AI agents status, pricing, rate-limit, and inference-performance data for AI tools, alongside agent-reported failure signals. It can also return incident activity and model-routing recommendations.
Key features
- 01Monitors status for 90+ AI tools
- 02Reports pricing for 300+ models
- 03Compares model costs for a token count
- 04Shows rate limits, quotas, and free tiers
- 05Provides latency, TTFT, and throughput data
- 06Accepts anonymous incident reports and returns routing advice
Use cases
- 01Choose a model that meets cost requirements
- 02Check whether an AI service is degraded or down
- 03Select models using latency and throughput data
- 04Decide whether to retry or switch models after an API error
MCP support
The server provides MCP clients with nine tools for pricing, status, incidents, rate limits, performance, and incident reporting. Clients that submit their own call status can receive richer latency data and live signals.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://tickerr.ai/mcpclaude mcp add --transport http imviky-ctrl-tickerr https://tickerr.ai/mcp{"mcpServers":{"imviky-ctrl-tickerr":{"type":"http","url":"https://tickerr.ai/mcp"}}}Open in Cursor{"servers":{"imviky-ctrl-tickerr":{"type":"http","url":"https://tickerr.ai/mcp"}}}Open in VS Code{"mcpServers":{"imviky-ctrl-tickerr":{"type":"http","url":"https://tickerr.ai/mcp"}}}{"mcpServers":{"imviky-ctrl-tickerr":{"serverUrl":"https://tickerr.ai/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMETickerr MCP
Outage radar for AI agents. LLM pricing, status, inference performance, and real-time agent-reported failure signals. 9 tools. No auth required.
Install
Claude Code
claude mcp add tickerr --transport http https://tickerr.ai/mcp
Cursor / Windsurf
{
"mcpServers": {
"tickerr": {
"url": "https://tickerr.ai/mcp"
}
}
}
Claude Desktop (stdio wrapper)
npx -y tickerr-mcp
Tools (9)
| 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 only without it. |
| 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. |
Give and Take
Tickerr MCP uses a give-and-take model across
get_tool_status, get_incidents, and report_incident.
get_tool_status — Unlock per-model latency
Always include my_status if your agent has made
any call to this provider in the last 5 minutes —
even successful calls. Pass is_up: true,
your response_ms, and the model name.
Basic response (no my_status):
- Operational verdict (OPERATIONAL / DEGRADED / DOWN)
- Response time, last check time, 30-day uptime
Full response (include my_status):
- Everything above +
- Per-model p50/p95 TTFT latency (last 24h)
- 90-day uptime, user reports signal
{
"slug": "claude",
"my_status": {
"is_up": true,
"response_ms": 1400,
"model": "claude-sonnet-4-6"
}
}
On failure, add error_code (e.g. "529").
is_up is the only required field.
get_incidents — Contribute to live signal
Include my_status when fetching incident history
to record your observation and see current
agent-reported activity alongside historical data.
Without my_status: incident list + prompt to contribute.
With my_status: incident list + live agent reports count + confirmation your signal was recorded.
report_incident — Get routing intelligence
report_incident — Get routing intelligence
What you get: When your agent calls report_incident
on a failure, you instantly receive:
- How many other agents reported the same issue
- Current signal state (detecting / confirmed / recovering)
- Which model to fall back to
What you give: Anonymous failure signal — provider, model, error code, latency only. No request content. No personal data.
How to use it (Claude Code):
report_incident fires automatically when your agent
hits a 5xx error. No configuration needed — the tool
description instructs Claude Code to call it.
How to use it (other agents): Add this to your system prompt:
On any 5xx error or timeout from an LLM API, call
report_incident from Tickerr MCP before retrying.
Use the returned recommendation to decide whether
to retry or switch models.
Reporting window: Active for 7 days from last Tickerr tool use. Renews automatically on any tool call.
Opt out any time: tickerr.ai/mcp/opt-out
Signal States
| State | Meaning | Reporter threshold |
|---|---|---|
| quiet | No reports in last 10 min | 0 |
| detecting | Reports coming in, not yet corroborated | 1–2 agents |
| confirmed | Issue verified by multiple agents | 3+ distinct agents |
| recovering | Reports dropping, recovery signals arriving | — |
Example Return Payload (report_incident)
REPORT RECEIVED
Provider: anthropic
Model: claude-haiku-3-5
Error: 529 overloaded
CURRENT SIGNAL (anthropic/claude-haiku-3-5)
Status: CONFIRMED
Agents reporting (last 10 min): 14
Total reports (last 10 min): 31
RECOMMENDATION
Action: FALLBACK
Switch to: gpt-4o-mini (openai)
REPORTING CADENCE
Next report for this model: in 3600 seconds if still failing.
Signal confirmed by multiple agents — reduce reporting frequency.
Data Coverage
- Status: 90+ AI tools monitored every 5 minutes
- Pricing: 300+ models, updated twice daily from OpenRouter and official provider docs
- Performance: Authenticated API latency checks every 5 minutes
- Agent signals: Live feed at tickerr.ai/agent-reports
Links
- Docs: tickerr.ai/mcp-server
- Status: tickerr.ai/status
- Pricing: tickerr.ai/pricing
- Agent reports: tickerr.ai/agent-reports
- Opt out: tickerr.ai/mcp/opt-out
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
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