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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it mentions rate limiting, parallel processing capability, performance considerations, and what the tool returns (availability status, pricing, summary statistics). It doesn't cover error handling or authentication requirements, but provides substantial operational 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 perfectly structured and concise. It starts with the core purpose, then provides operational details, return values, and a concrete example - all in 6 sentences with zero wasted words. Every sentence adds value and the information is front-loaded appropriately.

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 3 parameters, 100% schema coverage, but no annotations and no output schema, the description does well by explaining what the tool returns and providing an example. It covers the main use case comprehensively but could benefit from mentioning error cases or limitations (e.g., what happens if domains exceed 100).

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 baseline is 3. The description adds some value by reinforcing the 'domains' parameter's purpose ('domain names to check') and the 'tld' parameter's usage guidance ('single TLD for best performance'), but doesn't provide significant additional semantic context beyond what's already in the schema descriptions.

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 ('check availability', 'searches') and resources ('multiple domain names', 'up to 100 domains'). It distinguishes from siblings like 'search_domain' (singular) and 'suggest_domains' (suggestion rather than availability checking). The example reinforces the bulk nature of the operation.

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 ('multiple domain names at once', 'up to 100 domains in parallel') and offers performance guidance ('use a single TLD for best performance'). However, it doesn't explicitly state when NOT to use it or mention alternatives like 'search_domain' for single-domain checks, which would be needed for a perfect score.

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 effectively describes key traits: the tool checks availability across 10 platforms, returns a confidence indicator, and warns that some platforms (Instagram, LinkedIn, TikTok) have low confidence due to automated check blocks. It also clarifies default behavior for platforms when not specified. However, it does not mention rate limits, authentication needs, or error handling, leaving some gaps.

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 well-structured and front-loaded with the core purpose, followed by platform details and examples. Every sentence adds value: the first states the purpose, the second lists platforms with confidence levels, the third explains the return format, and the examples illustrate usage. There is no redundant or unnecessary information.

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 of checking multiple platforms with varying confidence levels and no output schema, the description is mostly complete. It covers the tool's purpose, supported platforms, confidence indicators, and usage examples. However, it lacks details on the return format structure, error scenarios, or how confidence levels are determined, which could be important for an agent to interpret results accurately.

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 description coverage is 100%, so the baseline is 3. The description adds significant value beyond the schema by explaining the platforms with confidence levels, providing examples of usage, and clarifying default behavior for the platforms parameter. This enhances understanding of how parameters affect the tool's operation, though it does not detail parameter interactions or edge cases.

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: 'Check if a username is available on social media and developer platforms.' It specifies the verb ('check'), resource ('username'), and scope ('social media and developer platforms'), distinguishing it from sibling tools like search_domain or suggest_domains which focus on domains rather than 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 description provides clear context for usage by listing supported platforms with confidence levels, which helps the agent understand when to rely on results. However, it does not explicitly state when to use this tool versus alternatives like bulk_search or compare_registrars, nor does it mention any prerequisites or exclusions beyond the confidence warnings for certain platforms.

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 describes what the tool returns (pricing comparison and recommendation) and the comparison criteria (first year price, renewal price, overall recommendation), but it doesn't mention potential limitations like rate limits, authentication requirements, or what happens with invalid inputs. The example adds some context but doesn't fully compensate for the lack of annotations.

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 well-structured and appropriately sized. The first sentence states the core purpose, followed by bullet points detailing what it checks, a sentence about returns, and a concrete example. Every sentence adds value without redundancy, and it's front-loaded with the main 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?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains the purpose and returns but doesn't cover error cases, output format details, or behavioral constraints. Without annotations or output schema, the agent lacks complete guidance on what to expect beyond the basic comparison promise.

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 input schema has 100% description coverage, providing clear documentation for all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3. The example shows parameter usage but doesn't explain semantics beyond the schema descriptions.

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 ('compare domain pricing') and resources ('across multiple registrars'), and it distinguishes from siblings by focusing on price comparison rather than availability checking (search_domain), bulk operations (bulk_search), or domain suggestion (suggest_domains). The example reinforces the specific comparison action.

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 (to compare pricing for a specific domain across registrars), but it doesn't explicitly state when not to use it or mention alternatives among the sibling tools. The example implies it's for single-domain comparisons, but there's no explicit exclusion for bulk operations or other use cases.

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 effectively describes what the tool returns (availability, pricing, WHOIS privacy, insights) and provides examples of usage, adding valuable context beyond the input schema. However, it doesn't mention rate limits, authentication needs, or error handling.

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 well-structured and front-loaded with the core purpose, followed by a clear bulleted list of returns and practical examples. Every sentence earns its place by enhancing understanding without redundancy, making it efficient and 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?

Given the complexity of a search tool with 3 parameters, no annotations, and no output schema, the description is largely complete—it explains the purpose, returns, and usage. However, it could improve by detailing output structure or error cases, slightly limiting completeness for agent invocation.

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 all parameters thoroughly. The description adds minimal value by illustrating parameter usage in examples (e.g., 'vibecoding' for domain_name, ['com', 'io'] for tlds), but doesn't provide additional semantic details beyond what the schema 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 ('Search for domain availability and pricing') and resource ('across multiple TLDs'), distinguishing it from siblings like 'check_socials' or 'suggest_domains'. It precisely defines the tool's function without being vague or tautological.

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 through examples (e.g., checking domain availability) but lacks explicit guidance on when to use this tool versus alternatives like 'bulk_search' or 'compare_registrars'. No context about exclusions or prerequisites is provided, leaving usage decisions 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. It discloses key behaviors: it generates variations (with specific types listed), checks availability, and returns only available ones ranked by quality. However, it doesn't mention potential limitations like rate limits, authentication needs, or what 'quality' ranking entails (e.g., based on length, memorability).

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 well-structured and appropriately sized: it starts with the core purpose, lists variation types with clear examples, states the return behavior, and ends with a concrete example. Every sentence adds value 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 no annotations and no output schema, the description is moderately complete. It covers the tool's purpose, behavior, and provides an example, but lacks details on output format (e.g., structure of returned suggestions), error handling, or deeper behavioral context like how 'quality' is determined.

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 baseline is 3. The description adds minimal parameter semantics beyond the schema: it implies 'base_name' is used for variation generation and provides example variations, but doesn't explain parameter interactions or default behaviors (e.g., what 'all' variants includes).

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 the tool's purpose: 'Generate and check availability of domain name variations' and 'Returns only available suggestions, ranked by quality.' It distinguishes from siblings like 'search_domain' (single check) and 'suggest_domains_smart' (likely more advanced) by specifying the variation generation approach with concrete examples.

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 (to get available domain variations from a base name) and includes an example. However, it doesn't explicitly state when not to use it or compare it to alternatives like 'suggest_domains_smart' or 'search_domain', which would be needed for a perfect score.

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 does well by detailing key behavioral traits: dual-source suggestions (semantic engine + GoDaddy AI), natural language understanding, auto-detection of industry, generation techniques (portmanteau, modern patterns), default filtering of premium domains, and pre-verified availability via GoDaddy. It lacks specifics on rate limits or error handling, but covers most operational aspects clearly.

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 and front-loaded, starting with a clear purpose statement followed by bullet-pointed features and examples. Every sentence adds value, though the bullet points could be slightly condensed. It efficiently communicates complex functionality without unnecessary fluff.

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 (6 parameters, AI-powered features) and no annotations or output schema, the description does a good job of completeness. It explains the tool's behavior, features, and provides examples, covering most contextual needs. A minor gap is the lack of output format details (e.g., structure of returned suggestions), but overall it's sufficient for an agent to understand and use the tool effectively.

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 6 parameters thoroughly. The description adds minimal parameter-specific semantics beyond the schema—it mentions 'industry' and 'style' in examples but doesn't elaborate on their effects beyond what the schema provides. This meets the baseline of 3 when schema coverage is high.

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 the tool's purpose: 'AI-powered domain name suggestion engine' that 'Generate[s] creative, brandable domain names from keywords or business descriptions.' It clearly distinguishes from siblings like 'search_domain' (likely direct search) and 'suggest_domains' (likely simpler suggestions) by emphasizing its 'smart' AI-powered approach with dual sources and advanced features.

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: for generating creative, brandable domain names from natural language queries, with examples showing different use cases. However, it does not explicitly state when not to use it or name specific alternatives among siblings (e.g., 'suggest_domains' vs. 'search_domain'), though the AI-powered features imply it's for more sophisticated suggestions.

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 of behavioral disclosure. While it lists return categories (description, price range, restrictions, popularity), it doesn't specify whether this is a read-only operation, if it requires authentication, rate limits, error conditions, or data freshness. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 efficiently structured: purpose statement, bulleted return categories, and a concrete example. Every sentence earns its place - the bullet points clarify what information is returned, and the example demonstrates usage. 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?

For a simple lookup tool with 2 parameters and 100% schema coverage but no output schema, the description is adequate but has gaps. It specifies what information is returned but not the format or structure. Without annotations, it should ideally mention that this is a read-only operation and any limitations. The example helps but doesn't fully compensate for missing behavioral 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 description coverage is 100%, so the schema already documents both parameters well. The description adds minimal value beyond the schema - it provides an example showing 'tld' parameter usage but doesn't explain the 'detailed' parameter's effect on the returned information. However, with only 2 parameters and excellent schema coverage, the baseline is high.

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 'Get information about a Top Level Domain (TLD)' - a clear verb ('Get information') and resource ('Top Level Domain'). It distinguishes from siblings like 'search_domain' or 'suggest_domains' by focusing on retrieving metadata about TLDs rather than searching for available domains or making suggestions.

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 no guidance on when to use this tool versus alternatives like 'compare_registrars' or 'suggest_domains'. There's no mention of prerequisites, use cases, or exclusions. The example shows usage but doesn't explain context or decision criteria for choosing this tool.

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