nutrition_performance_correlation

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

No annotations provided, so the description carries the burden. It explains the logic (correlation, splitting into high/low buckets) but does not disclose operational details like read-only nature, required data availability, or handling of missing data.

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 plus an Args block, front-loaded with a verb, and every sentence adds value. 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 tool's complexity (correlation analysis with bucket splitting) and the presence of an output schema, the description covers inputs and high-level logic. It lacks detail on edge cases or prerequisites but is sufficient for most agents.

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%, but the description adds format (YYYY-MM-DD) and sensible defaults (30 days ago, today) for both parameters, which is valuable beyond the bare schema types. However, there is a slight mismatch with schema defaults (empty strings).

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 it correlates nutrition intake with next-day training metrics, specifically protein/calorie patterns affecting body battery, HRV, and workout performance. It distinguishes itself from siblings like analyze_meal_patterns by focusing on correlation and splitting days into buckets.

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 when to use (to see correlation between diet and performance) but does not explicitly state when not to use or recommend alternatives among the many sibling tools like get_insights or analyze_meal_patterns.

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