AI agents use close_engagement to create or update resources in DefectDojo MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your DefectDojo MCP Server environment.
Closing an engagement changes its status in DefectDojo, which is a state modification. While this is a significant action (it marks security work as complete), it is reversible—an engagement can be reopened. This distinguishes it from Destructive (which would be permanent deletion).
From the tool's definition Tool name is 'close_engagement' with description 'Close an engagement'. In the context of DefectDojo (a vulnerability management tool), closing an engagement modifies its state reversibly—the engagement record is updated to a closed status but the data is not…
Documented attack patterns abuse exactly the kind of access close_engagement gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and DefectDojo MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for close_engagement:
{
"version": "1",
"default": "deny",
"tools": {
"close_engagement": {
"limits": [
{
"counter": "close_engagement_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}close_engagement 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.
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Close an engagement. It is categorised as a Write tool in the DefectDojo MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the DefectDojo MCP Server MCP server in PolicyLayer and add a rule for close_engagement: 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 DefectDojo MCP Server. Nothing to install.
close_engagement is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the close_engagement 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 close_engagement. 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.
close_engagement is provided by the DefectDojo MCP Server MCP server (jamiesonio/defectdojo-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from DefectDojo MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
11 DefectDojo MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.