# 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.

Agent View of the PolicyLayer registry record for `leave_a_note`. HTML page: https://policylayer.com/tools/com-ghostavo-retail-media-measurement/leave-a-note

## Facts

- Tool: `leave_a_note`
- Server: Gus Dantas · retail media measurement (`https://mcp.ghostavo.com/mcp`) — https://policylayer.com/tools/com-ghostavo-retail-media-measurement.md
- Homepage: https://github.com/https://mcp.ghostavo.com/mcp
- Risk category: Write (Medium risk)
- Registry record: grade C, identity unverified
- Server auth posture: open
- Server CORS policy: *
- Server rate-limited: no
- Parameters: 3 (1 required)
- Recommended policy verdict: Rate-limited

## Parameters

| Parameter | Type | Required | Description |
| --- | --- | --- | --- |
| `name` | string | no | Who the message is from. |
| `contact` | string | no | 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.

## Example call (MCP tools/call, JSON-RPC 2.0)

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "leave_a_note",
    "arguments": {
      "message": "<message>"
    }
  }
}
```

## 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.

## Use case

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.

## Recommended policy (PolicyLayer)

Verdict: **Rate-limited**. Enforced by the PolicyLayer MCP gateway (https://policylayer.com/mcp-gateway) before a call reaches Gus Dantas · retail media measurement:

```json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "leave_a_note": {
      "limits": [
        {
          "counter": "leave_a_note_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}
```

## Other tools on Gus Dantas · retail media measurement (3)

- `ask_gus` — Read — https://policylayer.com/tools/com-ghostavo-retail-media-measurement/ask-gus.md
- `gus_profile` — Read — https://policylayer.com/tools/com-ghostavo-retail-media-measurement/gus-profile.md
- `retail_media_primer` — Read — https://policylayer.com/tools/com-ghostavo-retail-media-measurement/retail-media-primer.md

## For agents

This record is a snapshot. Live verdicts and the full registry:

- Check every server in your MCP config at once: `npx -y policylayer stack`
- Vet a server before you add it: install the mcp-precheck skill — `npx skills add https://policylayer.com` (skill text: https://policylayer.com/skill.md)
- Query the registry over MCP: endpoint `https://api.policylayer.com/mcp` — tools `check_mcp_server`, `check_mcp_stack`, `check_tool`, `search_registry`, `get_change_events`

---

Source: the PolicyLayer MCP registry — one continuously verified record per MCP server. Full record: https://policylayer.com/registry?q=com-ghostavo-retail-media-measurement · API: https://policylayer.com/registry/api · Policy library: https://policylayer.com/policies/com-ghostavo-retail-media-measurement