AI agents use update_placement to create or update resources in AdButler — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your AdButler environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
id | number | Yes | Placement ID |
cost | object | — | Pricing model |
zone | object | — | Zone object (mutually exclusive with channel) |
active | boolean | — | Whether placement is active |
payout | object | — | Publisher payout configuration |
weight | number | — | Relative delivery weight |
channel | number | — | Channel ID (mutually exclusive with zone) |
keywords | string | — | Comma-separated keywords |
priority | string | — | Serving priority level |
schedule | number | — | Schedule ID |
geo_target | number | — | Geotarget ID |
list_target | number | — | List Target ID |
Parameters from the server's own tool schema.
The tool modifies an existing placement (reversible change to AdButler campaign/ad management data). This is a Write operation—it creates or modifies data reversibly. Severity is medium because changes to ad placements could affect campaign delivery and advertiser/publisher operations, but the change is reversible and does not involve financial transactions or irreversible deletion.
From the tool's definition Tool name is 'update_placement' and description states 'Update an existing placement', indicating modification of existing data without deletion.
Risk signalsHigh parameter count (32 properties)
Attacks that exploit this kind of access
Update an existing placement. It is categorised as a Write tool in the AdButler MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
update_placement accepts 12 parameters: id, cost, zone, active, payout, weight, channel, keywords, priority, schedule, geo_target, list_target. Required: id. The full parameter table on this page comes from the server's own tool schema.
Register the AdButler MCP server in PolicyLayer and add a rule for update_placement: 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 AdButler. Nothing to install.
update_placement 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 update_placement 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 update_placement. 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.
update_placement is provided by the AdButler MCP server (adbutler/mcp-server). 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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