optimize_lineup

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context beyond basic functionality: it mentions the tool 'returns multiple lineup variations with risk scores,' which hints at output behavior, and 'supports cash game and tournament strategies,' indicating different modes. However, it doesn't cover critical aspects like performance characteristics (e.g., computation time), error handling, or data freshness requirements for a complex optimization 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 appropriately sized with two sentences that are front-loaded with core functionality. The first sentence covers the main purpose and key features, while the second adds output details. There's no wasted text, though it could be slightly more structured (e.g., separating input and output aspects more clearly).

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 the tool's complexity (6 parameters, nested objects) and lack of annotations and output schema, the description is moderately complete. It covers the tool's purpose and hints at output behavior but lacks details on error conditions, performance, or how results are structured (beyond 'multiple lineup variations with risk scores'). For a sophisticated optimization tool, this leaves gaps in understanding full behavior.

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 high at 83%, so the baseline is 3. The description adds minimal parameter semantics beyond the schema: it mentions 'stacking preferences' (related to constraints.preferStacks) and 'player constraints' (related to constraints object), but doesn't provide additional meaning for key parameters like projections or salaryCap. It doesn't compensate for the 17% coverage gap in undocumented schema aspects.

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's purpose with specific verbs ('generate optimal DFS lineups using linear programming') and resources ('DFS lineups'), distinguishing it from sibling tools like get_player_projections (which provides input data) or explain_recommendation (which explains outputs). It explicitly mentions the optimization method and key features like cash/tournament strategies.

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 implies usage context by mentioning 'cash game and tournament strategies' and 'player constraints,' suggesting when different strategies might apply. However, it lacks explicit guidance on when to use this tool versus alternatives like get_player_projections (which provides input data) or explain_recommendation (which explains outputs), and doesn't specify prerequisites or exclusions.

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