build_cloud_flow
Use this when the user wants to build a brand-new CloudFlow automation from scratch using natural language. Creates the draft before planning, so flowId can be returned even if the builder stops early. Streams progress and returns flowId, conversationId, the answer, and any build steps. Reusing c...
This record as markdown: /tools/doit/build-cloud-flow.md
What build_cloud_flow does on Doit
AI agents invoke build_cloud_flow to trigger actions in Doit. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
| Parameter | Type | Required | Description |
|---|---|---|---|
question | string | Yes | Natural language description of the CloudFlow to build from scratch. |
conversationId | string | — | Optional conversation ID to continue an existing build session. |
Parameters from the server's own tool schema.
Why build_cloud_flow is rated High
build_cloud_flow triggers real processes with real consequences. An agent gone sideways doesn't fire it once. It starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Attacks that exploit this kind of access
The rule that runs build_cloud_flow 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 build_cloud_flow, this is the rule to start with:
build_cloud_flow stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. 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 build_cloud_flow call is checked against it from then on.
Questions about build_cloud_flow
Use this when the user wants to build a brand-new CloudFlow automation from scratch using natural language. Creates the draft before planning, so flowId can be returned even if the builder stops early. Streams progress and returns flowId, conversationId, the answer, and any build steps. Reusing conversationId continues the conversation but this endpoint still creates a new draft; refine_cloudflow targets an existing flow. Generated codeNode code can pass validation and fail silently or error at run time, so a successful build shows only that a draft was saved. export_cloudflow_flow returns the saved code; a completed test run's per-node output shows whether it works. Test runs execute real actions, including on drafts. It is categorised as a Execute tool in the Doit MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
build_cloud_flow accepts 2 parameters: question, conversationId. Required: question. 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 build_cloud_flow: 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.
build_cloud_flow is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the build_cloud_flow 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 build_cloud_flow. 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.
build_cloud_flow 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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