NameIntel

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

No annotations exist, so the description carries full burden. It implies a non-destructive read operation but does not detail side effects, API calls, rate limits, or whether data is real-time. Adequate but lacking depth.

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?

A single, clear sentence front-loads the action and resource. No extraneous 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?

For a simple two-parameter tool with no output schema, the description covers basic functionality. It could mention that it checks exact name matches per TLD or default TLDs, but is adequate for 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?

Schema coverage is 100% with parameter descriptions. The description reinforces the schema's 'brand name' concept and mentions 'pricing', adding minimal value beyond the schema. Baseline score 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 that the tool checks domain availability and pricing across multiple TLDs for a brand name. It uses a specific verb-resource combination and distinguishes itself from sibling tools by focusing on domains.

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 guidance is provided on when to use this tool versus its siblings (check_geo, check_social, check_trademark, score_name). The description only explains functionality, not usage context.

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 clearly states the tool evaluates multiple dimensions (entity collision, semantic distinctiveness, etc.), indicating a read-only analysis. However, it does not mention any potential side effects, authorization needs, or rate limits.

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 that immediately conveys the tool's purpose and scope, with no extraneous information. It is front-loaded and efficient.

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 single parameter and absence of output schema or annotations, the description provides adequate context about what the tool evaluates. However, it lacks information about the output format, such as whether it returns a numerical score or qualitative categories.

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 has 100% coverage with a basic description of 'name'. The tool description adds significant meaning by explaining that the evaluation includes entity collision, semantic distinctiveness, corpus saturation, and phonetic clarity, which goes beyond the schema's parameter description.

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 explicitly states 'Evaluate a brand name for AI findability (GEO score)' and lists the specific aspects assessed (entity collision, semantic distinctiveness, corpus saturation, phonetic clarity), clearly differentiating from sibling tools like check_domain or score_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 implies use when needing a GEO score for a brand name, but it does not provide explicit guidance on when to use this tool versus siblings (e.g., check_domain, check_social) or when not to use it.

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?

No annotations are provided, so the description must disclose behavioral traits. It only states a function without indicating read-only nature, destructive potential, rate limits, or other side effects.

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 concise sentence that effectively communicates the tool's core function. However, it could be more informative without becoming verbose.

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, so the description should explain what the result looks like (e.g., boolean availability, list of available platforms). It does not, leaving the agent without critical context.

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 coverage is 100%, with both parameters described. The description adds no additional meaning beyond what the schema provides, so 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 clearly states the verb 'Check' and the resource 'social media handle availability across 12 platforms', distinguishing it from siblings like check_domain or check_trademark.

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 guidance is provided on when to use this tool versus alternatives (e.g., check_domain for domains). The description lacks context for usage scenarios.

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?

No annotations are provided, so the description must carry the full burden. It discloses that the tool searches the USPTO database and returns results including risk level. However, it omits behavioral traits such as whether the tool is read-only, authentication requirements, rate limits, or what happens on errors. The description is basic but not misleading.

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 redundant information. It front-loads the action and resource, then lists return values efficiently. Every word serves a purpose.

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 low complexity (2 params, no output schema, no annotations), the description covers the essential purpose, scope, and output data. It mentions the USPTO database (US-specific) and Nice classes, which are key. It could mention that it only searches US trademarks, but that is implied by 'USPTO'. Nearly complete for a straightforward search tool.

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 both parameters having descriptive names and types. The description adds value by linking the 'niceClasses' parameter to 'Nice class information' in the output, and mentioning 'risk level' which is not in the schema. However, the added meaning is modest, so a 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 specifies the action ('Search USPTO trademark database') and the resource ('brand name conflicts'). It also lists what is returned (exact matches, similar marks, risk level, Nice class information). This effectively distinguishes it from siblings like check_domain, check_geo, check_social, which cover different domains.

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 when checking trademark conflicts for a brand name, but it does not explicitly state when to use this tool versus alternatives (e.g., check_domain for domain names). There is no guidance on when not to use it or prerequisites like trademark registration.

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?

No annotations provided, so the description carries full burden. It lists the five dimensions but does not disclose behavioral traits like data sources, computational cost, or side effects. The mention of 'AI findability (GEO score)' hints at methodology but lacks detail. Adequate but not rich.

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, well-structured sentence that concisely conveys the core function, output format, and dimensions. No redundant words; front-loaded with key action.

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 2 parameters and no output schema, the description adequately explains the return value (composite score 0-100 with sub-scores and verdict). It mentions all five dimensions, though slightly more detail on sub-scores could help. Overall complete for its complexity.

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%: both parameters have detailed descriptions. The tool description adds the purpose of scoring but does not enhance parameter meaning beyond the schema. Baseline 3 applies as 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 tool scores a brand name across 5 specified dimensions and returns a composite score 0-100 with sub-scores and verdict. The verb 'Score' and resource 'brand name' are specific, and the composite nature distinguishes it from sibling tools that check individual aspects.

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 comprehensive brand name evaluation, but does not explicitly state when to use it versus the sibling tools (e.g., for overall scoring vs. individual checks). No exclusions or alternatives are mentioned, but the context is clear enough.

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