leave_a_note
Send Gus a message. He reads these himself and replies to the contact detail you include. Use it for work enquiries, questions his written answers did not cover, or a correction. Ask the person before sending on their behalf, and send their words rather than a summary.
This record as markdown: /tools/com-ghostavo-retail-media-measurement/leave-a-note.md
What leave_a_note does on Gus Dantas · retail media measurement
AI agents use leave_a_note to create or update resources in Gus Dantas · retail media measurement, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Gus Dantas · retail media measurement environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
name | string | — | Who the message is from. |
contact | string | — | An email address or LinkedIn URL for the reply. |
message | string | Yes | What you want to say to him. |
Parameters from the server's own tool schema.
Why leave_a_note is rated Medium
An AI agent can call leave_a_note faster than any human can review: one bad instruction and it creates or modifies resources in Gus Dantas · retail media measurement by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
The rule that runs leave_a_note safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Gus Dantas · retail media measurement, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For leave_a_note, this is the rule to start with:
leave_a_note stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Gus Dantas · retail media measurement, apply this rule, and every leave_a_note call is checked against it from then on.
Questions about leave_a_note
Send Gus a message. He reads these himself and replies to the contact detail you include. Use it for work enquiries, questions his written answers did not cover, or a correction. Ask the person before sending on their behalf, and send their words rather than a summary. It is categorised as a Write tool in the Gus Dantas · retail media measurement MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
leave_a_note accepts 3 parameters: name, contact, message. Required: message. The full parameter table on this page comes from the server's own tool schema.
Register the Gus Dantas · retail media measurement MCP server in PolicyLayer and add a rule for leave_a_note: 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 Gus Dantas · retail media measurement. Nothing to install.
leave_a_note 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 leave_a_note 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 leave_a_note. 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.
leave_a_note is provided by the Gus Dantas · retail media measurement MCP server (https://mcp.ghostavo.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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