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

With no annotations, the description carries the full burden, and it does well by naming the exact return values and the diagnostic relationship to 402 errors. It could go further and explicitly state that calling this tool is read-only or does not consume credits, but the getter-style wording makes the behavior clear enough.

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 two sentences with no filler: the first names the return values, the second gives the key usage context. It is front-loaded and each sentence 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 zero-parameter tool with no output schema, the description is complete: it explains what the agent will get and when to call it. The added 402 context is especially useful for troubleshooting and tool selection.

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 tool has zero parameters, so the baseline is 4 and there are no parameter semantics to document. The description correctly focuses on what the tool returns rather than inputs.

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 states a specific action and resource: it returns remaining credits, the daily free quota, and the data version. This clearly distinguishes it from the sibling gender and name tools, which serve entirely different purposes.

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 gives an explicit trigger condition: when other tools fail with 402, call this tool to find out why. It does not mention when-not-to-use or alternatives, but as the only account/status tool among siblings, the omission is minor.

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, the description carries the full burden. It discloses batch capacity (up to 100), the summary return with match rate, and efficiency characteristics. However, it does not explain what happens on partial failures, what the full response shape looks like, or that values can be emails/usernames despite the type parameter.

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 sentences, both informative and front-loaded. The first sentence states capability and output, and the second gives a practical usage reason with no filler.

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?

The tool has no annotations, no output schema, and three parameters, so the description must carry more weight. It covers the core batch behavior and match-rate summary, but omits the type parameter's support for emails/usernames, the country override behavior, and a clearer picture of the response. These are useful for correct invocation but partially covered by the 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%, so the schema already documents the names, type, and country parameters. The description adds no parameter-level detail beyond what the schema provides, and does not mention the country weighting behavior or type flexibility, so the baseline of 3 is appropriate.

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 a specific action ('Resolves up to 100 values in one request') and distinguishes this tool from single-call alternatives by emphasizing bulk usage. However, it does not explicitly name sibling tools like gender_from_name or mention that the bulk call also supports emails and usernames, so differentiation is good but not complete.

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 says to use this tool for more than one name and explains that one-call-at-a-time is slower and wasteful. This gives clear context for when to choose the bulk endpoint, though it does not explicitly state 'use the singular sibling tools for one value' or enumerate 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, the description carries the behavioral disclosure burden. It discloses extraction from the local part, gender prediction, and the specific 'unknown' fallback for role addresses such as info@ or admin@. It does not enumerate all possible return values, but covers the main edge case.

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 a single focused sentence, front-loaded with the primary action and followed by a relevant edge-case note. Every phrase 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 simple two-parameter tool with full schema coverage, the description covers the main behavior, the fallback, and a notable special case. It would be slightly more complete with explicit alternate routing to siblings and an exact list of possible gender outputs, but nothing critical is missing.

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 parameters email and country are already documented well. The description adds context about local-part extraction but does not add significant per-parameter meaning beyond the schema, so baseline 3 applies.

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 states a specific verb and resource: 'Extracts a first name from the local part of an email address and predicts its gender.' It also clarifies the fallback to 'unknown', which helps distinguish it from sibling tools that operate on names or usernames.

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 input context is clear: this tool takes an email address and works on its local part. However, it does not explicitly say when to use this tool instead of gender_from_name, gender_from_username, or gender_bulk, leaving that routing to inference.

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 and does well: it discloses that the response includes probability, sample size, and match type, and that uncertain names return 'unknown' rather than a guess. It stops short of specifying the exact response shape or error behavior, but it is notably transparent for a read-only 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 three tight sentences with the purpose front-loaded. The follow-up details about response fields and the 'unknown' fallback are valuable and not redundant; every sentence 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 two-parameter read tool with no output schema, the description covers the purpose, the key response fields, and the uncertainty behavior. It could strengthen sibling differentiation, but nothing essential appears missing for an agent to call it correctly.

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 input schema already documents both the name and country parameters, including country-specific weighting. The description's mention of 'a person's name' adds no new parameter semantics beyond what the schema provides, so the baseline score of 3 applies.

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 opens with a specific verb and object: 'Predicts gender from a person's name.' This clearly distinguishes the tool from siblings like gender_from_email and gender_from_username, and the singular framing separates it from gender_bulk. The core function is unambiguous.

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 the tool is for name-based input, which differentiates it from email- and username-based siblings, but it never explicitly states when to choose this over alternatives. There are no conditions, exclusions, or routing guidance, so usage context is only implied rather than stated.

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 behavioral burden. It transparently discloses that the tool extracts a first name from the handle and then makes a gender prediction, which conveys that the result is probabilistic rather than authoritative. It does not describe edge cases like handles with no recognizable name, but the core behavior is clearly stated.

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 a single, front-loaded sentence with no filler or repetition. Every word contributes to understanding the tool's core function.

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?

The tool has no output schema, and the description does not specify the exact return format or behavior for ambiguous or name-less usernames. The description is adequate for a simple prediction tool, but it leaves some operational details to the agent's inference.

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 both parameters, including the country weighting behavior. The description adds little beyond restating that the input is a username or handle, so the baseline score of 3 is appropriate.

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 uses a specific verb ('Extracts') and resource ('a username or social media handle'), and clearly states the outcome ('predicts its gender'). It distinguishes itself from siblings like gender_from_email and gender_from_name by explicitly targeting usernames and handles.

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 intended input context is implied by 'username or social media handle', which signals when to use this tool rather than gender_from_name or gender_from_email. However, no explicit guidance is given about when not to use it, nor are sibling alternatives named.

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 discloses a critical behavioral trait: the ranking only compares seven countries, and countries without published counts appear in the attested list but not the ranking. This is valuable context beyond the schema. It does not mention pagination or exact return structure, but the core behavioral caveat is clearly disclosed.

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 two sentences with no wasted words. The core purpose is front-loaded, and the critical warning is placed immediately after. Every sentence earns its place, and the structure is easy to parse.

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 simple two-parameter tool with no output schema, the description covers the essential context: what the tool returns, the data source limitation, and the interpretation warning. It doesn't describe the exact return format, but given the tool's simplicity and the schema's completeness, the description is sufficiently complete for an agent to call it correctly.

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 both parameters ('name' and 'limit'). The description adds context about what 'limit' controls ('How many counted countries to return') implicitly through the ranking explanation, but it doesn't add new parameter-level detail beyond the schema. Baseline 3 is appropriate.

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 states a specific verb ('Returns') and resource ('countries where a name is recorded'), and immediately distinguishes itself from origin/ethnicity claims. The title 'Countries a name appears in' is reinforced by the description's explicit scope, making the tool's purpose unambiguous.

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 clearly explains the data source limitation (only seven countries publish counts) and warns against misinterpreting the ranking as origin/ethnicity. It does not explicitly name sibling tools as alternatives, but the warning effectively tells the agent when NOT to use this tool for origin/ethnicity questions, which is strong usage guidance.

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