run_managed_lesson_agent

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already indicate destructiveHint=true, and the description adds context about LLM mode requiring an API key and falling back to heuristics. However, it does not disclose what exactly is modified or created, nor the side effects of running the agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences. The first sentence front-loads the core purpose, and the second adds essential context about API key requirements and fallback. Every word is necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the core purpose and operational requirements (API key, fallback) but omits details about return values or what exactly is persisted. Given the tool is destructive and has no output schema, more detail on outcomes would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for all three parameters, so the baseline is 3. The description does not add any additional semantic information about the parameters beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Run' and the resource 'LLM-powered lesson inference and rule generation agent over accumulated feedback', making the purpose specific. However, it does not explicitly differentiate from sibling tools like 'infer_lesson_from_history', leaving ambiguity about when to use which.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lacks explicit guidance on when to use this tool versus alternatives. It mentions a fallback mode but does not provide criteria for choosing this tool over siblings like 'infer_lesson_from_history' or 'retrieve_lessons'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.