get_metric_definitions

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

With no annotations, the description carries the full burden and does so well: it states that these metrics are heuristic proxies rather than validated cognitive measurements, explains the role of DOIs under references, and warns that caveat sources must never be cited as support. This gives the agent essential trust and citation semantics before use.

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 compact and front-loaded, moving from the core purpose to the returned contents and then to the important citation caveat. Every sentence adds useful information without repetition.

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 zero-parameter schema, lack of output schema, and absence of annotations, the description is complete enough: it explains what the tool returns (formula, references with DOIs, caveat) and how to interpret the resulting guidance. No critical behavioral or usage detail is missing.

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 tool has zero parameters, so there is no parameter semantics to explain. The baseline for a zero-parameter tool is 4, and nothing in the description needs to compensate for missing schema detail.

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 uses a specific verb and resource: 'Explain each brain metric' and details that it returns exact formulas, literature with DOIs, and caveats. This clearly distinguishes it from sibling tools such as get_brain_state or get_band_powers, which retrieve live values rather than documentation.

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?

It gives a direct usage instruction: 'Call this to learn what focus/calm/attention/engagement/fatigue/meditation actually measure and how much to trust them.' It does not explicitly name alternatives or when not to use it, but the documentation role is clear enough for an agent to choose it appropriately.

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