llm_check_usage

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

Despite no annotations, the description discloses caching behavior, the JS snippet fallback with no page navigation, and downstream impact on model routing. It could be more explicit about read-only nature, but overall transparency is good.

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 three sentences, front-loading the main purpose and then adding details on caching and routing. No redundant information; every sentence adds value.

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?

Given no parameters and an output schema (assumed complete), the description covers the tool's purpose, caching behavior, fallback mechanism, and downstream use. It is sufficient for an agent to understand when and how to invoke this tool.

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?

With zero parameters, the baseline is 4. The description adds no parameter details because none exist, but the lack of parameters is clear from the schema. The description does not contradict or mislead.

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 tool checks real-time Claude subscription usage, mentioning specific limits like session and weekly limits. However, it does not explicitly differentiate from similar sibling tools like llm_usage or llm_quota_status, though the mention of real-time and JS snippet fallback provides implicit distinction.

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 explains when to use: for real-time usage check with caching and fallback to a JS snippet. It does not provide explicit guidance on when not to use or mention alternative tools, leaving the agent to infer context from the sibling list.

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