AI agents call review_learned to retrieve information from Project Tessera without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and displays previously auto-learned memories from the vector store. It performs no modifications, deletions, code execution, or side effects. The verb 'review' indicates inspection/viewing of existing data.
From the tool's definition Tool name 'review_learned' and description 'Review auto-learned memories' indicate read-only querying of stored memory data without modification or execution of external actions.
Documented attack patterns abuse exactly the kind of access review_learned gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Project Tessera, and nothing reaches the server without passing your rules. This is the rule we recommend for review_learned:
{
"version": "1",
"default": "deny",
"tools": {
"review_learned": {}
}
}review_learned is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Review auto-learned memories. It is categorised as a Read tool in the Project Tessera MCP Server, which means it retrieves data without modifying state.
Register the Project Tessera MCP server in PolicyLayer and add a rule for review_learned: 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 Project Tessera. Nothing to install.
review_learned is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the review_learned 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 review_learned. 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.
review_learned is provided by the Project Tessera MCP server (besslframework-stack/project-tessera). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Project Tessera, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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43 Project Tessera tools catalogued and risk-classified — across an index of 43,000+ MCP servers.