AI agents use update_product_source_target 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 |
|---|---|---|---|
name | string | — | Source target name |
source_id | number | — | Source ID to target |
publisher_id | number | Yes | Publisher ID |
source_target_id | number | Yes | Source target ID |
Parameters from the server's own tool schema.
This tool modifies an existing product source target, which is a write operation. It creates or modifies data reversibly (characteristic of Write category), as opposed to destructively deleting it. In an advertising campaign management system like AdButler, updating product source targets could affect ad delivery, pricing, or audience targeting, creating medium-severity risk if misused by an AI agent.
From the tool's definition Tool name 'update_product_source_target' and description 'Update an existing product source target' indicate modification of existing data without deletion. The 'update' verb denotes a write operation that is reversible.
Attacks that exploit this kind of access
Update an existing product source target. 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_product_source_target accepts 4 parameters: name, source_id, publisher_id, source_target_id. Required: publisher_id, source_target_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_product_source_target: 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_product_source_target 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_product_source_target 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_product_source_target. 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_product_source_target 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.
Teams ship this data inside their own products. See what a licence covers →