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

Annotations already include readOnlyHint=true, so no safety concerns. The description discloses additional behavioral traits: free tier limitations ('free without a key (a capped slice of each)'), premium tiers, and that it's based on a model. This adds value beyond annotations, but does not mention output format or pagination, which would be useful.

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 one substantial paragraph but front-loaded with the core value proposition, followed by filtering options and pricing tiers, and ends with example queries. It is information-dense but not overly long; every sentence adds value. Slightly long but appropriate for the tool's complexity.

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 tool with 5 optional parameters and no output schema, the description covers the core use case, filtering options, output scope (sleepers/busts), and pricing limitations. It does not detail output format (e.g., list vs. table), but the complexity is moderate. Given the annotation and schema richness, it is fairly complete.

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 schema description coverage is 100%, so the baseline is 3. The description adds some context for parameters (e.g., 'by league size, default 12-team' for round, 'Overrides platform default' for teams and scoring), but does not explain parameter interactions (e.g., how platform overrides scoring) beyond what the schema already states. Thus, it doesn't significantly enhance parameter understanding.

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 identifies the tool as one that shows 'biggest gaps' between a model and consensus ADP, distinguishing sleepers from busts. It includes specific context (standard, half-PPR, full-PPR) and examples of use cases, making the purpose clear. However, it does not explicitly name any sibling tool for differentiation, though the context makes it unique among the listed siblings.

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 rich usage context: it explains that the tool is for finding sleepers/busts, offers filtering by position or draft round, and gives example search queries. It implies when to use (when seeking undervalued/overvalued players) without explicit exclusions or naming alternatives. It doesn't explicitly say 'use instead of X', but the examples and context are strong enough.

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