update_iteration · Mcp Gitlab MCP: Risk & Policy

WHAT IT DOES

What update_iteration does on Mcp Gitlab

AI agents use update_iteration to create or update resources in Mcp Gitlab — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Mcp Gitlab environment.

WHY IT NEEDS A POLICY

Why update_iteration needs a policy

The tool creates or modifies data (an iteration/sprint record) in a reversible manner. This fits the Write category: it updates an existing object without deleting it. While iterations are project management constructs (not financial or destructive), a malicious agent could use it to corrupt sprint planning (disrupting team workflow).

From the tool's definition Tool description states 'Update an existing iteration (sprint)' — directly modifies an existing resource. The mention of 'dry_run=true by default' confirms this is a write operation with safety guardrails but still capable of reversible data modification.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

update_iteration Rate-limited

update_iteration stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "update_iteration": {
      "limits": [
        {
          "counter": "update_iteration_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Mcp Gitlab, so you can run agents against it safely.

FAQ

Questions about update_iteration

What does the update_iteration tool do? +

Update an existing iteration (sprint). The REST path uses iteration_iid (per-group), not the global id. dry_run=true by default. It is categorised as a Write tool in the Mcp Gitlab MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on update_iteration? +

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

What risk level is update_iteration? +

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

Can I rate-limit update_iteration? +

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

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

update_iteration is provided by the Mcp Gitlab MCP server (wanadev/gitlab-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.