aps_compute_data_axis_weights

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

No annotations are provided, so the description carries the full burden. It discloses that empty input yields empty array, all-zero raw weights cause error, and explains the weight formula (role × recency_decay × length_weight) normalized per spec. This provides good behavioral insight for a computation tool.

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 wasted words. It covers purpose, output, edge cases, formula, spec reference, and parameter naming. Highly concise and structured.

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 computation tool with no output schema, the description adequately explains the output type and behavior. It covers inputs, formula, normalization, and downstream use. Minor gaps include precise output format details but overall sufficient.

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 coverage is 100%, so the parameter descriptions are already present. The description adds that parameter names match the SDK and notes the optional profile defaults to DEFAULT_WEIGHT_PROFILE, but this is a minor addition. Baseline 3 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?

The description clearly states the tool computes a D-axis fractional weight vector from AccessReceipt records. It specifies the output type (DataAxisEntry[]), mentions the formula components and normalization, and explicitly ties it to aps_construct_attribution_primitive. This differentiates it from sibling tools like aps_compute_compute_axis_weights.

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 explains the tool's purpose and mentions edge cases (empty input, all-zero weights). However, it does not explicitly state when not to use it or compare to alternatives. The reference to the formula and output consumption provides contextual guidance.

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