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

The annotations already establish read-only, non-destructive, closed-world behavior, and the description adds substantial behavioral detail beyond that: the 12-week computation window, the specific analyses performed, the decision to stay silent on small samples, and the meaning of an empty answer. This gives the agent a clear model of what the tool will and will not do.

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, front-loads the core purpose, and each clause adds meaningful information: computation source, evaluated patterns, scope boundary, and empty-result semantics. No filler or repetition is present.

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 parameterless read-only tool, this is complete. It gives the computation window, the exact patterns being evaluated, the source data boundary ('no literature'), and a clear interpretation of empty output. Nothing needed to invoke or trust the tool 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 and schema description coverage is 100%, so there is no parameter burden for the description to carry. The baseline of 4 applies because nothing is underdocumented.

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 names a specific resource—this athlete's response profile—and explains exactly what it computes: whether quality sessions land as aimed, whether sRPE separates hard/easy days, whether low readiness predicts poor sessions, and which weekday leaks occur. This clearly distinguishes it from sibling read tools like get_readiness or get_load_summary.

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 clearly frames when this tool is appropriate: when you need evidence specific to this athlete from their own last 12 weeks, as opposed to literature or generalized knowledge. It also tells the agent that an empty answer means there is genuinely no usable sample yet. It does not explicitly name alternatives or exclusions, so it stops short of a 5.

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