correlate_metrics

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

With no annotations, the description must disclose behavioral traits. It mentions the output (strength, direction, significance) but omits details on the correlation method (e.g., Pearson vs Spearman), data handling (missing values, outliers), or error conditions (e.g., insufficient data points). Adequate but not transparent about internal mechanics.

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 extremely concise: two sentences that front-load the main action, provide an example, and summarize the return 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 low complexity (two metrics plus days) and no output schema, the description offers a basic understanding. However, it lacks details on return format, pagination, or error handling. Usage guidelines and behavioral transparency gaps reduce completeness. Adequate but not fully complete.

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 input schema already provides descriptions for all three parameters (metric1, metric2, days), achieving 100% coverage. The description adds no additional semantic value beyond the schema; it does not elaborate on parameter formats, units, or constraints. Baseline score of 3 applies.

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 purpose: finding correlations between two health metrics. It provides concrete examples (HRV with sleep duration, activity with readiness) and specifies the output (correlation strength, direction, statistical significance). This distinguishes it from sibling tools that focus on single-metric analysis or raw data retrieval.

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 through examples but does not explicitly state when to use this tool versus alternatives or when not to use it. No guidance on prerequisites, such as the need for sufficient data or the type of correlation used. Given the presence of sibling analysis tools, explicit usage guidelines would help an AI agent select correctly.

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