assign_staff_to_service
Assign a staff member to a booking service. Pass users.id from list_staff.userId.
This record as markdown: /tools/io-favcrm-favcrm/assign-staff-to-service.md
What assign_staff_to_service does on FavCRM
AI agents use assign_staff_to_service to create or update resources in FavCRM, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your FavCRM environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
userId | string | Yes | users.id from list_staff.userId |
serviceId | string | Yes | Service ID |
Parameters from the server's own tool schema.
Why assign_staff_to_service is rated Medium
This tool creates or updates a staff-to-service assignment within bookings, which is a write operation (data modification). It does not delete data (Destructive), execute arbitrary code (Execute), handle payments (Financial), or merely read data (Read). Severity is medium because misuse could disrupt service scheduling and customer bookings, but the effect is reversible by reassigning or removing the staff member.
From the tool's definition Tool description states 'Assign a staff member to a booking service' — this modifies booking-service relationships by adding staff assignments, which is a reversible data modification operation.
Attacks that exploit this kind of access
The rule that runs assign_staff_to_service safely
PolicyLayer is an MCP gateway: it sits between your AI agents and FavCRM, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For assign_staff_to_service, this is the rule to start with:
assign_staff_to_service 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 FavCRM, apply this rule, and every assign_staff_to_service call is checked against it from then on.
Questions about assign_staff_to_service
Assign a staff member to a booking service. Pass users.id from list_staff.userId. It is categorised as a Write tool in the FavCRM MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
assign_staff_to_service accepts 2 parameters: userId, serviceId. Required: userId, serviceId. The full parameter table on this page comes from the server's own tool schema.
Register the FavCRM MCP server in PolicyLayer and add a rule for assign_staff_to_service: 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 FavCRM. Nothing to install.
assign_staff_to_service 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 assign_staff_to_service 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 assign_staff_to_service. 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.
assign_staff_to_service is provided by the FavCRM MCP server (https://api.favcrm.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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