get_token_count

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

With no annotations, the description carries full burden for behavioral disclosure. It does reveal that the operation is an 'estimate' (not exact), but it fails to explain how the 'model' parameter affects tokenization, potential edge cases, or any errors. For a utility tool, this is insufficient.

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 two sentences, front-loaded with the core function, and contains no filler. Both sentences earn their place, making it highly concise and well-structured.

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?

Even though the tool is simple and an output schema exists, the description lacks critical context about the 'model' parameter and the estimation nature. It does not explain how the parameter influences results, nor does it provide any caveats about tokenization accuracy. This is a meaningful gap for correct usage.

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?

Schema description coverage is 0%. The description provides no explanation for either 'text' or 'model'. While 'text' is self-explanatory from the tool's purpose, the 'model' parameter is completely ambiguous—its effect on counting is not described, and the description does not compensate for the low schema coverage.

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's function with a specific verb ('Estimate') and resource ('token count for text'). It is distinct from sibling tools like chunk_text and truncate_text, which deal with text manipulation rather than measurement.

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 phrase 'Helps manage LLM context limits' implies a use case (checking token counts before sending to an LLM), but it does not explicitly state when to use this tool versus alternatives, nor does it mention when not to use it.

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