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dataflow_structure

For one ABS dataflow, return its ordered dimensions and the valid codes for each. Use this to build a dataKey for get_data: the key has one dot-separated position per dimension, in the order returned here. Call this before get_data.

Part of the Abs Au server.

dataflow_structure can trigger actions in Abs Au, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents invoke dataflow_structure to trigger processes or run actions in Abs Au. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.

dataflow_structure can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.

Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "dataflow_structure": {
      "limits": [
        {
          "counter": "dataflow_structure_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

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These attack patterns abuse exactly the kind of access dataflow_structure gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so dataflow_structure only ever does what you allow.

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Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the dataflow_structure tool do? +

For one ABS dataflow, return its ordered dimensions and the valid codes for each. Use this to build a dataKey for get_data: the key has one dot-separated position per dimension, in the order returned here. Call this before get_data.. It is categorised as a Execute tool in the Abs Au MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on dataflow_structure? +

Register the Abs Au MCP server in PolicyLayer and add a rule for dataflow_structure: 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 Abs Au. Nothing to install.

What risk level is dataflow_structure? +

dataflow_structure is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit dataflow_structure? +

Yes. Add a rate_limit block to the dataflow_structure 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.

How do I block dataflow_structure completely? +

Set action: deny in the PolicyLayer policy for dataflow_structure. 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.

What MCP server provides dataflow_structure? +

dataflow_structure is provided by the Abs Au MCP server (https://gateway.pipeworx.io/abs-au/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Abs Au tool call.

Deterministic rules across all 23 Abs Au tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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