list_gcp_resource_cuds
Evaluate current GCP commitments, plan and automate purchases, and optimize cloud costs with PerfectScale for Commitments. Returns a paginated list of resource-based (vCPU / memory) CUDs for the billing account. Optionally filter by CUD state (status). Omit to return CUDs in all states. This endp...
This record as markdown: /tools/doit/list-gcp-resource-cuds.md
What list_gcp_resource_cuds does on Doit
AI agents call list_gcp_resource_cuds to retrieve information from Doit without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
status | string | — | |
pageToken | string | — | |
maxResults | number | — | |
X-Tenant-Id | string | — | |
customerContext | string | — | Scope the request to a specific customer by ID. Required for DoiT employees (whose token isn't tied to a single customer); omit for direct customer users. |
billingAccountId | string | Yes |
Parameters from the server's own tool schema.
Why list_gcp_resource_cuds is rated Low
Even though list_gcp_resource_cuds only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
The rule that runs list_gcp_resource_cuds safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Doit, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For list_gcp_resource_cuds, this is the rule to start with:
list_gcp_resource_cuds is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Doit, apply this rule, and every list_gcp_resource_cuds call is checked against it from then on.
Questions about list_gcp_resource_cuds
Evaluate current GCP commitments, plan and automate purchases, and optimize cloud costs with PerfectScale for Commitments. Returns a paginated list of resource-based (vCPU / memory) CUDs for the billing account. Optionally filter by CUD state (status). Omit to return CUDs in all states. This endpoint is paginated: a response with a non-empty pageToken has more results, returned when that value is passed as the pageToken parameter. A missing, null, or empty pageToken marks the last page. It is categorised as a Read tool in the Doit MCP Server, which means it retrieves data without modifying state.
list_gcp_resource_cuds accepts 6 parameters: status, pageToken, maxResults, X-Tenant-Id, customerContext, billingAccountId. Required: billingAccountId. The full parameter table on this page comes from the server's own tool schema.
Register the Doit MCP server in PolicyLayer and add a rule for list_gcp_resource_cuds: 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 Doit. Nothing to install.
list_gcp_resource_cuds is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the list_gcp_resource_cuds 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 list_gcp_resource_cuds. 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.
list_gcp_resource_cuds is provided by the Doit MCP server (@doitintl/doit-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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