Update the success rate of a lesson after applying its solution
AI agents use update_lesson_success to create or update resources in Knowledge Graph Memory Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Knowledge Graph Memory Server environment.
This tool modifies a numeric metric (success rate) associated with a lesson, but the change is reversible and non-destructive. It stores learning metadata rather than executing arbitrary code or deleting data. The blast radius is limited to updating a single lesson's success counter, posing minimal risk even if misused by an AI agent.
From the tool's definition Tool name: update_lesson_success. Description: 'Update the success rate of a lesson after applying its solution.' The verb 'update' indicates modification of existing data (the success rate metric), which is a reversible write operation.
Documented attack patterns abuse exactly the kind of access update_lesson_success gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Knowledge Graph Memory Server, and nothing reaches the server without passing your rules. This is the rule we recommend for update_lesson_success:
{
"version": "1",
"default": "deny",
"tools": {
"update_lesson_success": {
"limits": [
{
"counter": "update_lesson_success_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_lesson_success stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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Update the success rate of a lesson after applying its solution. It is categorised as a Write tool in the Knowledge Graph Memory Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Knowledge Graph Memory Server MCP server in PolicyLayer and add a rule for update_lesson_success: 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 Knowledge Graph Memory Server. Nothing to install.
update_lesson_success 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 update_lesson_success 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 update_lesson_success. 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.
update_lesson_success is provided by the Knowledge Graph Memory Server MCP server (t1nker-1220/memories-with-lessons-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 13 Knowledge Graph Memory Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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13 Knowledge Graph Memory Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.