track_calories

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

The description has a comprehensive 'Behavioral Transparency' section detailing side effects (read-only, no side effects), authentication (no auth for basic, API key for pro), rate limits (10/day free, unlimited pro), error handling, idempotency, and data privacy. Since no annotations were provided, the description fully covers behavioral disclosure, exceeding expectations.

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 long but well-structured with clear sections (Args, Behavior, When to use, etc.) and front-loaded with the main purpose. There is some redundancy between the 'Behavior' and 'Behavioral Transparency' sections (both mention read-only and idempotency). Overall, sentences earn their place, but minor repetition prevents a perfect score.

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 complexity (4 parameters, no output schema, no annotations), the description covers inputs, behavior, and constraints well. However, it lacks description of the output format (only says 'analysis output'), and the 'api_key' parameter is not explained as an input. These gaps leave the agent uncertain about what the tool returns and how the api_key parameter is used.

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 schema has 4 parameters with 0% description coverage, so the description carries the full burden. It thoroughly explains the 'foods' parameter (list of dicts with keys: name, calories, and optional protein_g, carbs_g, fat_g, quantity) and the target parameters. However, the 'api_key' parameter in the schema is not explained in the description (though environment variable is mentioned). This is a minor gap.

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 tracks daily calorie and macronutrient intake from food entries, using a specific verb ('Track') and resource ('calorie and macronutrient intake'). It distinguishes itself from sibling tools (build_training_plan, etc.) which focus on exercise and body composition, not nutrition tracking.

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 includes dedicated 'When to use' and 'When NOT to use' sections. However, the 'When to use' is generic ('structured analysis or classification') and not specific to calorie tracking. The 'When NOT to use' appropriately warns against real-time production decisions without human review. No explicit comparison to sibling tools, but the domain is clearly different.

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