AI agents use learn_stamp to create or update resources in SkillMCP — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your SkillMCP environment.
The tool creates directories and updates a stamp file, which are reversible write operations. It does not delete data, execute arbitrary code, or have financial implications. The blast radius is low as it only manages metadata/stamp files for the learning system.
From the tool's definition 'Ensure .learn output dirs exist and update lean.stamp after a learn run'
Attacks that exploit this kind of access
Ensure .learn output dirs exist and update lean.stamp after a learn run. It is categorised as a Write tool in the SkillMCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Skill MCP server in PolicyLayer and add a rule for learn_stamp: 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 SkillMCP. Nothing to install.
learn_stamp 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 learn_stamp 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 learn_stamp. 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.
learn_stamp is provided by the Skill MCP server (nventimiglia/skillsmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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