nationalize

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the batch nature, request limit (10 names), and output format ('ranked nationality probabilities for each name'). However, it lacks details on error handling, rate limits, or authentication needs, leaving some gaps in behavioral context.

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 conveys key details in two concise sentences. Every sentence earns its place by specifying the action, scope, limit, and output, with no wasted words or 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 tool's moderate complexity (batch prediction with a limit), no annotations, and no output schema, the description is largely complete. It covers purpose, usage, behavioral traits (batch, limit, output format), and parameters. However, it could improve by detailing error cases or response structure, slightly reducing completeness.

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%, with the schema fully documenting the 'names' parameter (array of strings, max 10 items). The description adds minimal value beyond the schema, mentioning 'multiple first names' and 'up to 10 names', but does not provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting.

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 ('Predict nationalities') and resource ('multiple first names'), distinguishing it from the sibling tool 'predict_nationality' by emphasizing batch processing ('multiple...in a single request') and the limit ('up to 10 names'). It avoids tautology by not merely repeating the tool name.

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 explicitly states when to use this tool: for processing 'multiple first names in a single request' with a 'maximum 10 names'. This directly contrasts with the sibling tool 'predict_nationality', which likely handles single names, providing clear alternative usage guidance without needing explicit exclusions.

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. It discloses key behavioral traits: returns ranked results by probability, up to 5 country codes with scores. However, it doesn't mention accuracy limitations, data sources, rate limits, or error conditions that would be helpful for a prediction 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 perfectly concise: two sentences with zero wasted words. The first sentence states the purpose, the second specifies the output format. Every element earns its place.

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 single-parameter prediction tool with no annotations and no output schema, the description is adequate but has gaps. It explains what the tool does and the output format, but doesn't address confidence thresholds, limitations, or what the country codes represent (ISO codes?).

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 fully documents the single 'name' parameter. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for high schema coverage.

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 ('predict'), resource ('nationalities for a given first name'), and scope ('most likely... ranked by probability'). It distinguishes from the sibling tool 'batch_predict' by specifying this is for a single name, not batch processing.

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: when you need nationality predictions for a single first name. It doesn't explicitly state when not to use it or mention the sibling 'batch_predict' as an alternative, but the single-name focus is implied.

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