AI agents use send_email to create or update resources in Gg — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Gg environment.
Sending email creates new data (messages) that persist and reach external recipients, making it a Write action. Severity is high because unauthorized email sending can damage reputation, enable phishing/spam, or impersonate the user. The empty description limits confirmation of exact behavior, but the name and server context make the classification clear.
From the tool's definition Tool name 'send_email' from Gmail-enabled server (gg-mcp connects to Google Workspace including Gmail); function is to send emails, which creates new messages in recipient inboxes.
Attacks that exploit this kind of access
send_email. It is categorised as a Write tool in the Gg MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Gg MCP server in PolicyLayer and add a rule for send_email: 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 Gg. Nothing to install.
send_email 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 send_email 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 send_email. 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.
send_email is provided by the Gg MCP server (tannht/google-cloud-mcp). 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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