gemma_worker_run_task
A execute tool on the Mapi Agent Memory MCP server.
This record as markdown: /tools/mapi-agent-memory/gemma-worker-run-task.md
What gemma_worker_run_task does on Mapi Agent Memory
AI agents invoke gemma_worker_run_task 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_task is rated High
The tool name contains 'run_task', which is characteristic of Execute category tools that trigger external operations. Although the description is empty (lowering confidence slightly), the naming convention strongly indicates code or task execution. Given the governance context, misuse could allow an agent to execute arbitrary tasks outside intended parameters.
From the tool's definition Tool name 'gemma_worker_run_task' indicates execution of a task via a worker process. The context of an 'auditable memory and governance' system for AI agents suggests this tool triggers external task execution whose effects depend on arguments provided.
Attacks that exploit this kind of access
The rule that runs gemma_worker_run_task 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_task, this is the rule to start with:
gemma_worker_run_task 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_task call is checked against it from then on.
Questions about gemma_worker_run_task
gemma_worker_run_task 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_task: 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_task 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_task 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_task. 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_task 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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