Update job status/result. Status: queued/claimed/running/done/failed/cancelled.
AI agents use hive.job_update to create or update resources in Mnemos — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Mnemos environment.
This is a Write operation as it creates or modifies data reversibly—updating job status is a state change that could theoretically be reverted by updating again. It does not irreversibly delete data (Destructive), execute arbitrary code (Execute), or move money (Financial).
From the tool's definition Tool explicitly updates job status to various states (queued/claimed/running/done/failed/cancelled), modifying persistent job records in the hive job queue system.
Documented attack patterns abuse exactly the kind of access hive.job_update gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Mnemos, and nothing reaches the server without passing your rules. This is the rule we recommend for hive.job_update:
{
"version": "1",
"default": "deny",
"tools": {
"hive.job_update": {
"limits": [
{
"counter": "hive.job_update_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}hive.job_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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Update job status/result. Status: queued/claimed/running/done/failed/cancelled. It is categorised as a Write tool in the Mnemos MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Mnemos MCP server in PolicyLayer and add a rule for hive.job_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 Mnemos. Nothing to install.
hive.job_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 hive.job_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 hive.job_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.
hive.job_update is provided by the Mnemos MCP server (ncz-os/mnemos). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Mnemos, 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.
15 Mnemos tools catalogued and risk-classified — across an index of 43,000+ MCP servers.