explain_recommendation

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

With no annotations provided, the description carries full burden. It discloses the tool's behavioral traits: it provides SHAP-based explainability for AI decisions, shows feature contributions, and helps understand model reasoning. However, it doesn't mention potential limitations, computational cost, or what happens with different explainerType values beyond the enum options.

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 perfectly front-loaded with the core purpose in the first sentence, followed by supporting details. Every sentence earns its place by adding value - the second sentence elaborates on what the tool shows, and the third provides usage context. No wasted words.

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 4 parameters with 100% schema coverage but no output schema or annotations, the description is adequate but could be more complete. It explains the tool's purpose well but doesn't describe the return format (e.g., structured data with feature contributions, visualizations as base64). For a tool with no output schema, more detail about what to expect would be helpful.

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 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema - it mentions SHAP values (matching the default explainerType) and visualization aspects, but doesn't provide additional semantic context about parameter interactions or usage patterns.

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 ('Get detailed explainability', 'Shows which features contributed most') and resources ('AI projection decisions', 'player's projection'). It distinguishes from siblings by focusing on explainability rather than getting projections, odds, or optimizing lineups.

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 ('Perfect for understanding the "why" behind projections'), but doesn't explicitly state when not to use it or name specific alternatives. It implies usage when explanation is needed rather than just getting projections.

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