get_cloudflow_flow_run
Manage CloudFlow. Returns a run's status and, for each node, the JSON it consumed and produced. This is how you find out *why* a run failed, or that it "succeeded" while producing the wrong data. input is null for most node types, and that is not an error. Only action nodes — the AWS, GCP, Azure,...
This record as markdown: /tools/doit/get-cloudflow-flow-run.md
What get_cloudflow_flow_run does on Doit
AI agents call get_cloudflow_flow_run 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 |
|---|---|---|---|
runId | string | Yes | |
flowId | string | Yes | |
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. |
Parameters from the server's own tool schema.
Why get_cloudflow_flow_run is rated Low
Even though get_cloudflow_flow_run 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.
Risk signalsBulk/mass operation — affects multiple targets · Admin/system-level operation
Attacks that exploit this kind of access
The rule that runs get_cloudflow_flow_run 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 get_cloudflow_flow_run, this is the rule to start with:
get_cloudflow_flow_run 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 get_cloudflow_flow_run call is checked against it from then on.
Questions about get_cloudflow_flow_run
Manage CloudFlow. Returns a run's status and, for each node, the JSON it consumed and produced. This is how you find out *why* a run failed, or that it "succeeded" while producing the wrong data. input is null for most node types, and that is not an error. Only action nodes — the AWS, GCP, Azure, Oracle, DoiT and admin operations — record their inputs. Transform, code, branch, switch, datastore, subflow and trigger nodes record none, so their input is always null. output is recorded by every node that finishes, so read a transform's behaviour from its output. Payloads appear as soon as a node reports a terminal status, so a poll loop can read results while later nodes are still running. Any value the node's schema marks sensitive is replaced with a redaction marker; credentials and connection configuration never appear. Each input and output is capped at 64KB. When a payload exceeds that, whole entries are dropped from the end, truncated is true, and totalBytes reports the untruncated size — nothing is silently cut, and what you receive is always valid JSON. Runs belonging to another tenant, or to a different flow, return 404. Nodes inside a fan-out currently report only the last path to finish. It is categorised as a Read tool in the Doit MCP Server, which means it retrieves data without modifying state.
get_cloudflow_flow_run accepts 3 parameters: runId, flowId, customerContext. Required: runId, flowId. 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 get_cloudflow_flow_run: 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.
get_cloudflow_flow_run 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 get_cloudflow_flow_run 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 get_cloudflow_flow_run. 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.
get_cloudflow_flow_run 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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