Return an agent-facing ledger that classifies observed session events into save, ignore, needs-evidence, or already-distilled memory decisions.
AI agents call kage_learning_ledger to retrieve information from Kage without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool returns a classification ledger of session events — this is a read/query operation that retrieves and categorizes existing data without modifying, creating, or deleting anything. Blast radius is low since it only surfaces decision classifications to the agent.
From the tool's definition Return an agent-facing ledger that classifies observed session events into save, ignore, needs-evidence, or already-distilled memory decisions
Documented attack patterns abuse exactly the kind of access kage_learning_ledger gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Kage, and nothing reaches the server without passing your rules. This is the rule we recommend for kage_learning_ledger:
{
"version": "1",
"default": "deny",
"tools": {
"kage_learning_ledger": {}
}
}kage_learning_ledger is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Return an agent-facing ledger that classifies observed session events into save, ignore, needs-evidence, or already-distilled memory decisions. It is categorised as a Read tool in the Kage MCP Server, which means it retrieves data without modifying state.
Register the Kage MCP server in PolicyLayer and add a rule for kage_learning_ledger: 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 Kage. Nothing to install.
kage_learning_ledger 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 kage_learning_ledger 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 kage_learning_ledger. 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.
kage_learning_ledger is provided by the Kage MCP server (@kage-core/kage-graph-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Kage, 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.
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