estimate_tokens

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

No annotations are provided, so the description carries full burden. It only states it uses 'calibrated chars/token ratios per model' but does not disclose error handling (e.g., unsupported models), limitations (e.g., max text length), or side effects (none expected). Minimal behavioral context.

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?

Two concise sentences plus an example. Every part is valuable: verb, resource, technique, example. No wasted words.

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 the presence of an output schema (not shown but mentioned), the description does not need to detail return values. It covers the core functionality but misses context about model compatibility, potential errors, and differentiation from siblings. For a tool of this simplicity, a 3 is adequate.

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 coverage is 0% (no descriptions in input schema). The description adds that the model parameter influences the number of tokens via 'calibrated chars/token ratios' and provides an example showing default model and output shape. However, it does not explain the text parameter's format or constraints, and the model parameter's supported values are not listed. Adequate but not comprehensive.

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 ('Estimate token count') and resource ('text string'), with an example that demonstrates usage. It distinguishes itself from sibling tools like compress_prompt and analyze_context by focusing solely on token estimation.

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 implies usage via the example but does not explicitly state when to use this tool versus alternatives. It lacks guidance on scenarios like when to use estimate_tokens vs compress_prompt or route_model.

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