This record as markdown: /tools/aiwerk-mcp-server-ghl/users-update-user.md
What users_update_user does on Ghl
AI agents use users_update_user to create or update resources in Ghl, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Ghl environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
role | string | — | |
type | string | — | |
email | string | — | Email update is no longer supported due to security reasons. |
phone | string | — | |
scopes | array | — | Scopes allowed for users. Only scopes that have been passed will be enabled. If passed empty all the scopes will be get disabled |
userId | string | Yes | Path parameter userId. |
lastName | string | — | |
password | string | — | |
companyId | string | — | Company/Agency Id. Required for Agency Level access |
firstName | string | — | |
locationIds | array | — | |
permissions | object | — |
Parameters from the server's own tool schema.
Why users_update_user is rated Medium
An AI agent can call users_update_user faster than any human can review: one bad instruction and it creates or modifies resources in Ghl by the hundred, each call as confident as the last.
Risk signalsHandles credentials or secrets (password) · High parameter count (54 properties)
Attacks that exploit this kind of access
The rule that runs users_update_user safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Ghl, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For users_update_user, this is the rule to start with:
users_update_user 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 Ghl, apply this rule, and every users_update_user call is checked against it from then on.
Questions about users_update_user
Update User. It is categorised as a Write tool in the Ghl MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
users_update_user accepts 12 parameters: role, type, email, phone, scopes, userId, lastName, password, companyId, firstName, locationIds, permissions. Required: userId. The full parameter table on this page comes from the server's own tool schema.
Register the Ghl MCP server in PolicyLayer and add a rule for users_update_user: 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 Ghl. Nothing to install.
users_update_user 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 users_update_user 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 users_update_user. 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.
users_update_user is provided by the Ghl MCP server (@aiwerk/mcp-server-ghl). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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