Generate and persist a structured debugging report.
AI agents use debug_report to create or update resources in M3 Memory — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your M3 Memory environment.
The tool creates and stores a new debugging report (persists it), which is a Write operation. It does not delete data or execute code, but it does create new persistent data in the memory layer. Severity is medium because misuse could flood storage with spurious reports or leak sensitive debug information.
From the tool's definition Generate and persist a structured debugging report
Documented attack patterns abuse exactly the kind of access debug_report gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and M3 Memory, and nothing reaches the server without passing your rules. This is the rule we recommend for debug_report:
{
"version": "1",
"default": "deny",
"tools": {
"debug_report": {
"limits": [
{
"counter": "debug_report_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}debug_report 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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Generate and persist a structured debugging report. It is categorised as a Write tool in the M3 Memory MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the M3 Memory MCP server in PolicyLayer and add a rule for debug_report: 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 M3 Memory. Nothing to install.
debug_report 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 debug_report 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 debug_report. 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.
debug_report is provided by the M3 Memory MCP server (skynetcmd/m3-memory). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from M3 Memory, 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.
43 M3 Memory tools catalogued and risk-classified — across an index of 43,000+ MCP servers.