Medium Risk

move_step

Move a step to a new position in the workflow's steps array. Provide exactly one target: - insertAfter: place the step immediately after the given step ID. - position: "first": place the step at index 0. Use this to put a trigger back in first position after a remove + add cycle (the only way to ...

Part of the Agentled server.

move_step can modify Agentled data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use move_step to create or modify resources in Agentled. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call move_step repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Agentled.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "move_step": {
      "limits": [
        {
          "counter": "move_step_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

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

Browse the full MCP Attack Database →

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

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

What does the move_step tool do? +

Move a step to a new position in the workflow's steps array. Provide exactly one target: - insertAfter: place the step immediately after the given step ID. - position: "first": place the step at index 0. Use this to put a trigger back in first position after a remove + add cycle (the only way to recover trigger order via MCP — add_step always appends at the end). - position: "last": place the step at the end of the array. Only the array order changes — NO next pointers or step config are modified. This is a pure cosmetic reorder that fixes the "orchestrator-issue" validator warning caused by steps being stored out of execution-chain order. Use this when the validator reports: "Step X appears after Step Y but executes before it. Reorder the steps array..." Works for both live workflows (via draft snapshot) and draft workflows.. It is categorised as a Write tool in the Agentled MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on move_step? +

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

What risk level is move_step? +

move_step is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit move_step? +

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

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

move_step is provided by the Agentled MCP server (@agentled/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Agentled tool call.

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