gemma_worker_run_job
A execute tool on the Mapi Agent Memory MCP server.
This record as markdown: /tools/mapi-agent-memory/gemma-worker-run-job.md
What gemma_worker_run_job does on Mapi Agent Memory
AI agents invoke gemma_worker_run_job to trigger actions in Mapi Agent Memory. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why gemma_worker_run_job is rated High
The tool appears to execute jobs via a worker system, which constitutes running code or triggering external operations (Execute category). Given the governance context of the parent server (auditable memory for AI agents), misuse of an unmonitored job execution could have significant side effects. High severity is appropriate for a job runner that could execute arbitrary workloads.
From the tool's definition Tool name 'gemma_worker_run_job' indicates execution of a job or task. The 'run' verb combined with 'job' suggests the tool triggers external operations or computations whose effects depend on job parameters.
Attacks that exploit this kind of access
The rule that runs gemma_worker_run_job safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mapi Agent Memory, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For gemma_worker_run_job, this is the rule to start with:
gemma_worker_run_job stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mapi Agent Memory, apply this rule, and every gemma_worker_run_job call is checked against it from then on.
Questions about gemma_worker_run_job
gemma_worker_run_job is a execute tool on the Mapi Agent Memory MCP server. It is categorised as a Execute tool in the Mapi Agent Memory MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Mapi Agent Memory MCP server in PolicyLayer and add a rule for gemma_worker_run_job: 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 Mapi Agent Memory. Nothing to install.
gemma_worker_run_job is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the gemma_worker_run_job 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 gemma_worker_run_job. 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.
gemma_worker_run_job is provided by the Mapi Agent Memory MCP server (cabo0m/mapi-agent-memory). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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