Read a bounded time series

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

With no annotations provided, the description carries the behavioral disclosure burden. It reveals non-obvious behaviors: deterministic cursor pagination and automatic stream downsampling, which go beyond the schema. It does not mention auth, rate limits, or return envelope, but the disclosed traits are material and genuinely helpful.

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?

Three dense sentences with every clause earning its place. The main operation is front-loaded, followed by constraints and prerequisites, with no filler or repetition of schema details.

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 10 parameters, no output schema, and no annotations, the description covers critical prerequisites and key constraints, making it minimally viable for straightforward reads. However, return shape, pagination limits, default downsampling behavior, and dataset naming conventions remain undocumented, which are notable gaps at this complexity level.

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?

Schema description coverage is 0%, so the description must compensate for all 10 parameters. It usefully explains metrics, filters, and athleteId sourcing, but leaves cursor, pageSize, resolution, dataset, activityId, startDate, and endDate to be inferred from names and enums. This is only partial compensation.

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 ('Read') and resource ('cataloged numeric series'), immediately distinguishing it from analysis, aggregation, and description siblings. 'Bounded time series' plus pagination and downsampling adds concrete operational specificity.

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?

Provides clear usage conditions: metrics must be numeric catalog columns, identifiers and strings belong in filters, call describe_dataset for uncertainty, and athleteId comes from list_athletes. It lacks explicit 'when not to use' guidance or direct comparison with analyze_series/aggregate_data, 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.