learning_batch_status

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

With no annotations, the description carries the full burden and does disclose a meaningful behavioral trait: this is a cached, local-only read that triggers no network or model activity. However, it does not state read-only semantics explicitly, data freshness, or what 'compact state' contains, leaving gaps for a status tool.

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?

One short, front-loaded sentence with no wasted words. It is efficient, though its terseness borders on cryptic and could have added a few words of purpose without becoming verbose.

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?

In a large batch/exam/learning ecosystem, this zero-param status tool needs to clarify which batch state it reports and how it relates to start_learning_batch, stop_learning_batch, resume_learning_batch, and parallel_learning_status. It also lacks an output schema, so the description should say more about what 'compact state' includes.

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 tool takes zero parameters, so there are no argument semantics to explain. The schema is empty and fully described; the description adds no parameter confusion, matching the baseline for a zero-param tool.

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 is a constraint fragment ('Cached compact state only; no page reads, HTTP calls or model API') rather than a purpose statement. It never says it returns the status of a learning batch; an agent must infer the action from the tool name alone.

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?

It implicitly signals a cheap cached read by excluding page reads, HTTP calls and model API use, but gives no when-to-use guidance, no exclusions, and does not name alternatives such as parallel_learning_status or inspect_current_task.

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