llm_refresh_claude_usage

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

With no annotations, the description carries full behavioral burden. It details the steps: reading token from keychain, calling API, updating cache. This is transparent for a simple tool, though it could explicitly state that it overwrites the local cache.

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 with no wasted words. The first sentence states the core purpose, followed by implementation details, and ends with a prerequisite. Perfectly front-loaded and concise.

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 zero parameters and the presence of an output schema (not shown but indicated), the description covers the tool's purpose, process, and prerequisites completely. It is fully adequate for agent understanding and invocation.

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?

There are no parameters, so schema coverage is 100%. The description adds value by explaining the tool's operation without needing parameters, earning the baseline 4.

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 'Refresh' and the resource 'Claude subscription usage'. It distinguishes from sibling tools by specifying the OAuth API method, no browser requirement, and the keychain token reading process.

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 provides context for when to use (refreshing usage without browser) and mentions prerequisites (Claude Code installed and authenticated on macOS). However, it does not explicitly exclude alternatives or compare to related tools like llm_check_usage or llm_update_usage.

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