Mark an external update as acknowledged (seen by DM). Use when you
AI agents use acknowledge_update to create or update resources in DMCP — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your DMCP environment.
This tool modifies game state by marking an update record as acknowledged—a reversible write operation with no side effects beyond recording that an action has been taken. It does not delete data (Destructive), execute arbitrary code (Execute), move money (Financial), or retrieve data without modification (Read).
From the tool's definition Tool name 'acknowledge_update' and description 'Mark an external update as acknowledged' indicate a state change operation that records that the DM has seen/processed an update.
Documented attack patterns abuse exactly the kind of access acknowledge_update gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and DMCP, and nothing reaches the server without passing your rules. This is the rule we recommend for acknowledge_update:
{
"version": "1",
"default": "deny",
"tools": {
"acknowledge_update": {
"limits": [
{
"counter": "acknowledge_update_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}acknowledge_update 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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Mark an external update as acknowledged (seen by DM). Use when you. It is categorised as a Write tool in the DMCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the D MCP server in PolicyLayer and add a rule for acknowledge_update: 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 DMCP. Nothing to install.
acknowledge_update 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 acknowledge_update 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 acknowledge_update. 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.
acknowledge_update is provided by the D MCP server (shawnrushefsky/dmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from DMCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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204 DMCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.