complete_booking

Mark a booking as completed. Triggers commission calculation if configured.

SERVERFavCRM SOURCEhttps://api.favcrm.io/mcp

Medium RISK CLASS

Category Write

Parameters 11 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-favcrm-favcrm/complete-booking.md

WHAT IT DOES

What complete_booking does on FavCRM

AI agents use complete_booking 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
`bookingId`	string	Yes	The booking ID to complete

Parameters from the server's own tool schema.

RISK

Why complete_booking is rated Medium

This tool modifies existing booking data by changing its status to completed, making it a Write operation rather than Read. While it triggers commission calculations (which could have financial implications), the primary action is reversible status modification, not irreversible deletion or direct financial transaction.

From the tool's definition Tool description states "Mark a booking as completed" which is a state modification operation. The mention of "Triggers commission calculation if configured" indicates it causes side effects (commission calculations) but these are reversible through system…

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs complete_booking 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 complete_booking, this is the rule to start with:

complete_booking Rate-limited

complete_booking 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "complete_booking": {
      "limits": [
        {
          "counter": "complete_booking_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect FavCRM, apply this rule, and every complete_booking call is checked against it from then on.

FAQ

Questions about complete_booking

What does the complete_booking tool do? +

Mark a booking as completed. Triggers commission calculation if configured. 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.

What parameters does complete_booking accept? +

complete_booking accepts 1 parameter: bookingId. Required: bookingId. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on complete_booking? +

Register the FavCRM MCP server in PolicyLayer and add a rule for complete_booking: 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.

What risk level is complete_booking? +

complete_booking is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit complete_booking? +

Yes. Add a rate_limit block to the complete_booking 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.

How do I block complete_booking completely? +

Set action: deny in the PolicyLayer policy for complete_booking. 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.

What MCP server provides complete_booking? +

complete_booking 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.

KEEP EXPLORING

More on FavCRM, and thousands of servers like it.

This server

All 220 FavCRM tools →

Across the catalogue

reminders_complete on Apple Apps MCP → The MCP Attack Database →

Guides

Govern customer-ops agents →Argument validation →Scoped token →