AI agents use create_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 |
|---|---|---|---|
cost | object | — | Pricing model: fixed_cost OR cpm/cpc/cpa |
zone | object | — | Zone object (required if channel not set; mutually exclusive with channel) |
active | boolean | — | Whether placement is active (defaults to true) |
payout | object | — | Publisher payout configuration |
weight | number | — | Relative delivery weight |
channel | number | — | Channel ID (required if zone not set; mutually exclusive with zone) |
keywords | string | — | Comma-separated keywords for targeting |
priority | string | — | Serving priority level (defaults to "standard") |
schedule | number | Yes | Schedule ID |
geo_target | number | — | Geotarget ID |
list_target | number | — | List Target ID |
serve_method | string | — | Serving system: weight-based or auction-based |
Parameters from the server's own tool schema.
This tool creates new placements which are reversible configuration changes to the advertising system. It does not execute external code, delete data irreversibly, or move money. The blast radius is medium because incorrect placements could affect ad serving behavior and campaign performance, but changes can be undone by removing or modifying the placement.
From the tool's definition Tool description states 'Create a new placement (assign an ad item or campaign to a zone or channel)'. The verb 'create' and 'assign' indicate data creation and modification operations.
Risk signalsHigh parameter count (31 properties)
Attacks that exploit this kind of access
Create a new placement (assign an ad item or campaign to a zone or channel). 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.
create_placement accepts 12 parameters: cost, zone, active, payout, weight, channel, keywords, priority, schedule, geo_target, list_target, serve_method. Required: schedule. 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 create_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.
create_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 create_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 create_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.
create_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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