AI agents use update_demand_source 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 | Demand source ID |
name | string | — | Demand source name |
status | string | — | Whether to send bid requests |
spend_limit | number | — | Spend limit amount |
spend_limit_end | string | — | Spend limit end date (ISO-8601) |
spend_limit_start | string | — | Spend limit start date (ISO-8601) |
spend_limit_period | string | — | Spend limit reset interval |
Parameters from the server's own tool schema.
This tool creates or modifies data (a demand source configuration) in a reversible manner. It does not delete data (Destructive), execute arbitrary code (Execute), move money (Financial), or retrieve data without side effects (Read). Update operations are classified as Write.
From the tool's definition Tool name 'update_demand_source' and description 'Update an existing demand source' indicate modification of existing data. The 'update' verb and context within an ad management system (AdButler) confirm this is a Write operation that modifies configuration.
Attacks that exploit this kind of access
Update an existing demand source. 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_demand_source accepts 7 parameters: id, name, status, spend_limit, spend_limit_end, spend_limit_start, spend_limit_period. 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_demand_source: 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_demand_source 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_demand_source 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_demand_source. 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_demand_source 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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