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

Annotations already indicate read-only and non-destructive behavior, so the bar for additional disclosure is lower. The description adds valuable context by specifying exactly what the baseline contains (7d/28d mean, 28d SD, rolling-3d, sample size), which helps the agent interpret results. It does not describe output structure or potential edge cases, but for a zero-parameter read-only tool this is sufficient.

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 two sentences with no filler. The first sentence front-loads the core factual definition of the baseline, and the second sentence adds a practical usage note. Every phrase earns its place, and the formatting is easy to scan.

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?

For a zero-parameter, read-only tool with clear annotations, the description provides enough context for an agent to call and interpret the tool correctly. It defines the data contents and the intended comparison target. No output schema exists, but the description enumerates the key return fields, so no critical information 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?

There are no parameters, and schema coverage is 100%, so nothing needs to be documented about inputs. The baseline score for zero-parameter tools is 4, and the description does not need to compensate for parameter ambiguity. No further parameter semantics are required.

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 identifies the resource: the athlete's current per-metric readiness baselines, including specific summary statistics like 7d/28d mean, 28d SD, and sample size. It communicates that this is the athlete's 'personal normal,' which is a clear and meaningful purpose. However, it does not explicitly name or contrast sibling tools like get_readiness_today or get_readiness, so sibling differentiation is only implicit.

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 provides clear usage context: compare today's readiness values against these baselines and never against population absolutes. This gives the agent actionable guidance on how to use the returned data. It does not explicitly name alternative tools or state when not to use this tool, but the population-vs-personal contrast effectively steers usage.

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