get_brain_state

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

With no annotations, the description carries the full disclosure burden and does a good job: it names the confidence fields, the per-metric confidence, and the status enum including 'warming_up', which warns the agent that early readings are not trustworthy. It omits any mention of latency, rate limits, or whether calling it triggers sampling, but the core behavioral caveats are present.

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?

Front-loaded with the verb and resource, then the return payload, then the confidence caveat, then the follow-up pointer. Every sentence adds information an agent needs; nothing is padded or repeated from structured fields.

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?

No output schema exists, so the description must explain the return shape, and it does — metrics, band powers, signal quality, confidence fields, and status values. With zero parameters and no annotations, this is sufficient for an agent to call and correctly interpret the result.

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 takes zero parameters, so there is nothing to disambiguate; the baseline for a parameterless tool applies. The description spends no words on irrelevant input discussion, which is appropriate.

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?

States a specific verb and resource ('Get the current brain state') and enumerates what comes back: proxy metrics, band powers, signal quality. A sibling like get_band_powers or get_signal_quality overlaps with parts of the return payload, and the description never clarifies why an agent would choose this composite read over those narrower tools, so it stops short of full sibling differentiation.

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?

Gives real conditional guidance: weight metrics by confidence and 'do not interpret an unreliable/low-confidence reading', plus a pointer to get_metric_definitions for interpretation. It does not say when to prefer this tool over get_band_powers or get_signal_quality, so there is no explicit alternative routing.

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