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run_stream

Starts an already created stream, specified by the provided resource 'name' parameter. Parameters * 'name': The resource name of the stream to start. * 'name' should be in the format of: 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locati...

Risk signalsBulk/mass operation — affects multiple targets

Part of the Mcp server.

run_stream can trigger actions in Mcp, 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 run_stream to trigger processes or run actions in Mcp. 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.

run_stream 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": {
    "run_stream": {
      "limits": [
        {
          "counter": "run_stream_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

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These attack patterns abuse exactly the kind of access run_stream 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 run_stream 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 run_stream tool do? +

Starts an already created stream, specified by the provided resource 'name' parameter. Parameters * 'name': The resource name of the stream to start. * 'name' should be in the format of: 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locations/us-central1/streams/my-streams'. * 'force': Whether to run the stream without running prior configuration verification. The default is 'false'. Returns * This tool returns a long-running operation. Use the 'get_operation' tool with the returned operation name to poll its status until it completes. Operation may take several minutes; do not check more often than every ten seconds.. It is categorised as a Execute tool in the Mcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on run_stream? +

Register the MCP server in PolicyLayer and add a rule for run_stream: 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 Mcp. Nothing to install.

What risk level is run_stream? +

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

Can I rate-limit run_stream? +

Yes. Add a rate_limit block to the run_stream 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 run_stream completely? +

Set action: deny in the PolicyLayer policy for run_stream. 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 run_stream? +

run_stream is provided by the MCP server (https://datastream.googleapis.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Mcp tool call.

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