Analyze costs broken down by model for a given team over a configurable time window.
Part of the Ai Cost Optimizer server.
Free to start. No card required.
AI agents invoke model_breakdown to trigger processes or run actions in Ai Cost Optimizer. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
model_breakdown can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"model_breakdown": {
"limits": [
{
"counter": "model_breakdown_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Ai Cost Optimizer policy for all 5 tools.
These attack patterns abuse exactly the kind of access model_breakdown gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Analyze costs broken down by model for a given team over a configurable time window.. It is categorised as a Execute tool in the Ai Cost Optimizer MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Ai Cost Optimizer MCP server in PolicyLayer and add a rule for model_breakdown: 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 Ai Cost Optimizer. Nothing to install.
model_breakdown is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the model_breakdown 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 model_breakdown. 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.
model_breakdown is provided by the Ai Cost Optimizer MCP server (https://api.lazy-mac.com/ai-cost-optimizer/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 Ai Cost Optimizer 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.