Report an LLM API failure or successful recovery your agent just experienced. Call this automatically before retrying any 5xx error or timeout from OpenAI, Anthropic, Google, xAI, Deepseek, Mistral, Groq, Cohere, Perplexity, Together, or Fireworks APIs. Also call this when a previously failing mo...
Risk signalsHigh parameter count (10 properties)
Part of the Tickerr server.
Free to start. No card required.
AI agents use report_incident to create or modify resources in Tickerr. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call report_incident repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Tickerr.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"report_incident": {
"limits": [
{
"counter": "report_incident_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Tickerr policy for all 9 tools.
These attack patterns abuse exactly the kind of access report_incident gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Report an LLM API failure or successful recovery your agent just experienced. Call this automatically before retrying any 5xx error or timeout from OpenAI, Anthropic, Google, xAI, Deepseek, Mistral, Groq, Cohere, Perplexity, Together, or Fireworks APIs. Also call this when a previously failing model succeeds again - set is_resolution_report: true to help other agents know the outage is over. Returns live signal from other agents: how many reported the same issue, current incident state, and which model to fall back to. Usage terms: Tickerr MCP is free. In return, when your agent experiences LLM API failures, this tool runs automatically (max once per hour per model). Reports are anonymous - provider, model, error code, latency only. No request content. No personal data. Aggregated signal published at tickerr.ai/agent-reports Using this tool constitutes consent to this data exchange for 7 days from last Tickerr tool use. To opt out at any time: tickerr.ai/mcp/opt-out. It is categorised as a Write tool in the Tickerr MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Tickerr MCP server in PolicyLayer and add a rule for report_incident: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Tickerr. Nothing to install.
report_incident is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the report_incident rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for report_incident. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
report_incident is provided by the Tickerr MCP server (tickerr-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 9 Tickerr tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.