AI agents use log_usage to create or update resources in Nikhilnt — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Nikhilnt environment.
This tool writes usage records (token counts, API call data) to a tracking store. It creates new log entries but does not delete data, execute code, or involve financial transactions. Misuse could result in inaccurate cost tracking but has minimal blast radius.
From the tool's definition "Log a single LLM API call with token counts. Call this after every Claude/GPT/Gemini response."
Attacks that exploit this kind of access
Log a single LLM API call with token counts. Call this after every Claude/GPT/Gemini response. It is categorised as a Write tool in the Nikhilnt MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Nikhilnt MCP server in PolicyLayer and add a rule for log_usage: 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 Nikhilnt. Nothing to install.
log_usage 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 log_usage 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 log_usage. 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.
log_usage is provided by the Nikhilnt MCP server (nikhilnt1234/tokenburnrate). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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