Generate a structured learning plan for a medical topic.
AI agents use generate_learning_plan to create or update resources in MedAdapt Content Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your MedAdapt Content Server environment.
This tool creates new educational content/plans but does not execute external code, delete data, or perform financial transactions. The severity is medium rather than low because if an AI agent generates a flawed or inappropriate learning plan, it could mislead medical learners, potentially affecting educational outcomes or clinical competency.
From the tool's definition The tool 'generate_learning_plan' creates a structured output artifact (a learning plan) that represents new data generated by the system.
Documented attack patterns abuse exactly the kind of access generate_learning_plan gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MedAdapt Content Server, and nothing reaches the server without passing your rules. This is the rule we recommend for generate_learning_plan:
{
"version": "1",
"default": "deny",
"tools": {
"generate_learning_plan": {
"limits": [
{
"counter": "generate_learning_plan_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}generate_learning_plan 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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Generate a structured learning plan for a medical topic. It is categorised as a Write tool in the MedAdapt Content Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the MedAdapt Content Server MCP server in PolicyLayer and add a rule for generate_learning_plan: 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 MedAdapt Content Server. Nothing to install.
generate_learning_plan 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 generate_learning_plan 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 generate_learning_plan. 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.
generate_learning_plan is provided by the MedAdapt Content Server MCP server (ryoureddy/medadapt-content-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from MedAdapt Content Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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7 MedAdapt Content Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.