squiggle_ladder

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

Annotations already mark the tool as read-only, idempotent, and open-world, so no safety contradiction exists. The description adds valuable behavioral context beyond annotations: it explains that 'rank' is projected (not actual), describes the 'swarms' distribution, and notes the data size implication (~100 KB) and that no auth is needed. This enriches the agent's understanding without repeating annotation properties.

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 compact and well-structured: a one-sentence purpose, a structured return format, an example query, and an auth note. Every sentence contributes unique information, and the return payload is clearly formatted with field names, making it scannable and free of filler.

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?

Despite having no output schema, the description includes the full return structure, semantics of key fields, an example invocation, and authentication requirements. It also addresses the practical concern of data size and differentiates from a sibling tool, making the description self-sufficient for an agent to invoke the tool correctly.

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 input schema already provides descriptions for all parameters (100% coverage), so the baseline is 3. The description goes further by advising 'q' is fixed ('Leave as-is') and recommending the 'round' parameter based on data size ('Recommended — a season is ~100 KB'), adding practical meaning that aids parameter selection.

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 opens with a specific verb and resource: 'PROJECTED end-of-season ladder per model', clarifying this returns simulated projections, not actual standings. It explicitly distinguishes itself from squiggle_standings by stating 'rank here is PROJECTED, unlike squiggle_standings' and introduces the unique 'swarms' field, making the tool's purpose unmistakable.

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 context for when to use this tool: it is for projected ladders, directly contrasting with squiggle_standings. It also offers parameter guidance ('round: Projection as at this round. Recommended — a season is ~100 KB'), helping the agent decide on parameter values. However, it does not explicitly state when *not* to use it or name other alternatives beyond squiggle_standings.

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