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.

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

With no annotations provided, the description carries full burden but lacks critical behavioral details. It mentions 'real-time' and 'line movement history' but doesn't specify update frequency, rate limits, authentication requirements, or data freshness guarantees. For a tool with no annotation coverage, this leaves significant gaps in understanding operational behavior.

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?

Two efficient sentences with zero waste. The first sentence clearly states purpose and output, while the second adds valuable context about odds comparison. Every element serves a purpose, and the description is appropriately front-loaded with core functionality.

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 4-parameter tool with no annotations and no output schema, the description provides adequate purpose clarity but insufficient behavioral context. It covers what the tool does but lacks details on how it behaves operationally, what the return format looks like, or any limitations. The 100% schema coverage helps, but the description should do more to compensate for missing annotations and output schema.

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%, providing good parameter documentation. The description adds marginal value by implying 'multiple sportsbooks' relates to the bookmakers parameter and 'current spreads, totals, moneylines, and player props' relates to markets. However, it doesn't provide additional syntax, format, or constraint details beyond what the schema already specifies.

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 specific action ('Get real-time betting odds') and resource ('from multiple sportsbooks'), with detailed output specification ('spreads, totals, moneylines, and player props with line movement history'). It distinguishes from siblings by focusing on odds retrieval rather than projections, recommendations, or lineup optimization.

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?

No explicit guidance on when to use this tool versus alternatives is provided. The description mentions 'Identifies best available odds across bookmakers,' which implies a comparison function, but doesn't specify when to choose this over other tools like get_player_projections or explain_recommendation. No exclusions or prerequisites are mentioned.

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

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 mentions the tool returns projections with confidence scores and SHAP explainability, but lacks details on permissions, rate limits, data freshness, or error handling, which are critical for a tool with 8 parameters and no output schema.

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 front-loaded with the core purpose and efficiently lists key return values in a single, well-structured sentence. Every part earns its place by clarifying what the tool delivers without redundancy.

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 complexity (8 parameters, no annotations, no output schema), the description is adequate but incomplete. It covers the purpose and return types but lacks behavioral context and output details, making it minimally viable but with clear gaps for effective tool use.

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 by implying the tool handles filtering and performance limits, but does not provide additional syntax, format, or usage context for parameters.

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 specific action ('Get AI-powered DFS player projections') and resource ('player projections'), distinguishing it from sibling tools like 'explain_recommendation', 'get_live_odds', and 'optimize_lineup' by focusing on projection retrieval rather than explanation, odds, or lineup optimization.

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?

No explicit guidance on when to use this tool versus alternatives is provided. The description does not mention any prerequisites, exclusions, or comparisons to sibling tools, leaving the agent to infer usage based on the purpose alone.

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

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.