Vee3

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

Describes return format (JSON object with booleans) and cost (5 tokens). No annotations provided, so description carries full burden; it covers basic behavior but no mention of side effects or safety.

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 plus cost note. Highly efficient, front-loaded with action and constraints.

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 tool with 1 parameter and output schema, description fully explains behavior and return value. No gaps relative to 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 coverage is 100% with parameter description. Description adds no additional meaning beyond 'up to ten domains', which is already in schema (maxItems). 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?

Clearly states 'Check domain name availability' and specifies batch size (up to ten). Distinct from sibling tools like domains.dns_records or domains.lookup.

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?

Tells when to use (check availability) and implicitly different from other domain operations, but does not explicitly mention alternatives or when not to use.

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 the cost (2 tokens), return type (array with fields), and optional subdomain behavior. It does not explicitly state that it is read-only or detail error conditions, but the 'Fetch' verb implies no 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 four sentences, front-loaded with the purpose. Each sentence adds value: purpose, return structure, optional parameter, cost. No redundant or extraneous 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 presence of an output schema, the description adequately covers purpose, return structure, optional parameter, and cost. It lacks guidance on error cases or prerequisites, but overall is sufficient for effective tool 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 coverage is 100% with both parameters already well-described. The description adds an illustrative example for the 'subdomain' parameter but does not fundamentally enhance understanding beyond the schema. Baseline is 3.

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 'Fetch DNS records for a domain', specifying the verb and resource. It distinguishes from sibling tools like domains.whois or domains.check_availability by focusing exclusively on DNS records. The return structure and optional subdomain parameter add further clarity.

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 implicitly indicates when to use this tool (when DNS records are needed), but does not explicitly mention alternatives or exclusion criteria. However, the context of sibling tool names provides implicit differentiation, making the usage context clear.

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 description carries burden. Discloses pagination defaults (limit=100), cost, and availability values. Does not explicitly label as read-only, but context suggests it's safe. Good behavioral context beyond 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?

Concise, front-loaded with primary action, then filtering details, then specific example of values. No wasted words. Each sentence 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 5 optional parameters and output schema, description covers all parameters, default behavior, filtering options, and output contents. Cost and availability clarification further complete the picture.

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 covers 100% of parameters (baseline 3). Description adds value by explaining availability values ('general-availability' vs 'sunrise') and listing output fields. Repetition of default limit is minor; overall adds meaning.

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?

Clear verb+resource: 'List or search top-level domains (TLDs) with registry metadata.' Immediately distinguishes from siblings like domains.tld_details (single TLD) and domains.check_availability (availability check).

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?

Implied usage via filtering and pagination, but no explicit when-to-use vs alternatives like tld_details for detailed info or check_availability for domain names. Lacks exclusion criteria.

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 exist, so the description carries full burden. It discloses that the tool combines multiple sources (registry/registrar RDAP and WHOIS) and normalizes results, and also notes the cost of 5 tokens. This adequately describes the tool's 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?

The description is only 4 sentences, front-loaded with the main purpose, then details and usage hint. Every sentence adds value, 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 the simple one-parameter tool and that an output schema exists, the description fully covers what the tool does, its data sources, usage context, and cost. It is complete for an AI agent to correctly select and invoke it.

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 a clear parameter description ('Domain name to look up (for example example.com).'). The tool description does not add additional parameter-level meaning beyond what the schema provides, 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 states 'Look up a domain and return normalized registration details' and elaborates on combining RDAP and WHOIS to output specific fields (availability, dates, registrar, etc.). This distinguishes it from sibling tools like domains.rdap or domains.whois which return raw data.

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?

Explicitly advises 'Use this when you need registrant-oriented summary data rather than raw WHOIS or RDAP payloads.' This provides clear when-to-use guidance and contrasts with raw data alternatives.

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 description carries full burden. It discloses return data comprehensively and mentions token cost (40 tokens), but does not explicitly state it's read-only or note any authentication requirements. This is adequate but not exhaustive.

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?

Description is a single paragraph that front-loads the purpose and then details metrics. It is reasonably concise but could benefit from bullet points or separation to improve scannability.

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 output schema exists and the description covers key return categories, it is fairly complete. It doesn't explain error conditions or token cost breakdown, but for a data retrieval tool, it provides sufficient context for an AI agent.

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% for the domain parameter, with a clear description in the schema. The description adds context about analyzing comprehensive metrics but does not provide additional syntax or format details beyond what the schema offers. 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?

Description clearly states the tool analyzes a domain with comprehensive SEO metrics, backlink data, and social signals, and explicitly lists the types of metrics returned. This distinguishes it from sibling tools like domains.whois or domains.dns_records.

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?

Description implies usage for deep domain analysis but lacks explicit guidance on when not to use it or mention of alternative tools like seo.basic_metrics. The comprehensive metric list suggests it's for full SEO analysis, but no direct comparison given.

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 behaviors. It mentions the data returned and cost (3 tokens), which is useful, but lacks info on authentication, rate limits, error handling, or 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 three short sentences plus a cost line. It front-loads the main action and efficiently lists returns. 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?

With one parameter, an output schema exists (though not shown), and no annotations, the description covers the essential aspects: purpose, returns, cost. It lacks error conditions or prerequisites but is adequate for a simple lookup.

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% and the parameter's description is clear. The tool description adds little beyond the schema, reiterating the domain name purpose. 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 it looks up raw RDAP data for a domain and lists the returned data types. It distinguishes from sibling tools like domains.whois by specifying 'RDAP' but does not explicitly contrast.

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 does not specify when to use this tool over alternatives like domains.whois or domains.lookup. Usage context is implied by the RDAP keyword but no explicit guidance is given.

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 exist, so the description must disclose behavioral traits. It mentions cost but fails to address side effects, authentication, rate limits, or that the operation is read-only.

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 four sentences, front-loaded with purpose, lists returns, gives a usage hint, and mentions cost. No unnecessary 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 single-parameter tool with an output schema, the description covers purpose, parameter format, return fields, and cost. It lacks guidance on sibling differentiation but is otherwise complete.

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 parameter 'tld' is fully described in the schema with 100% coverage. The description adds an example (com) but largely restates the schema, offering minimal extra meaning.

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 it looks up detailed registry information for a TLD, lists specific return fields, and distinguishes from sibling tools by focusing on TLD-level registry data.

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 for TLD registry lookups but does not compare to sibling tools like domains.lookup or domains.whois, nor provide when-not-to-use guidance.

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 description carries full burden. It explains return format (JSON keyed by WHOIS server, parsed fields, raw text), parameter effect (include_registrar speed/complete trade-off), and cost. No contradictions.

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?

Three sentences: purpose, return structure, parameter guidance, plus cost. Front-loaded and each sentence adds value.

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 output schema exists (not shown but hinted), description adequately covers return content (parsed fields, raw text). Complete for a lookup tool with two parameters.

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 has 100% description coverage, and the description adds meaning: explains include_registrar trade-off (slower but more complete) and mentions cost. Adds value beyond schema.

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 fetches WHOIS registration data for a domain, with a specific verb ('Fetch') and resource ('WHOIS registration data'). It distinguishes from siblings like domains.check_availability and domains.dns_records by focusing on registration data.

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?

Explains when to use include_registrar (true for complete data, slower; false by default) and notes cost. However, it doesn't compare explicitly with alternatives like domains.rdap or provide when-not-to-use guidance.

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 the output format as a map of language names to codes and notes the cost of 1 token. No side effects or restrictions are mentioned, but for a simple read-only listing, the description is sufficiently transparent.

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?

Three concise sentences: purpose, usage, and cost. Every sentence adds value with no redundant or extraneous information. Front-loaded effectively.

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 simplicity (no parameters, output schema exists), the description is complete. It explains what it does, the output format, how to use the output with other tools, and the cost. No gaps.

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 and 100% schema coverage, so the baseline is 4. The description does not need to add parameter info, and it does not.

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 'List supported language codes for Google Maps place endpoints' with a specific verb and resource. It distinguishes itself from sibling tools like google-maps.nearby_search or google-maps.place_details by being a utility that provides language codes for use with those endpoints.

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 explains 'Use these codes with the language parameter on place detail, review, and photo calls,' providing clear context for when to use the tool. It does not explicitly state when not to use it or list alternatives, but the tool name and scope implicitly differentiate it from google-search.languages.

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 provides behavioral insights such as pagination (cursor), cost (10 tokens), and that additional upstream fields may appear. However, it does not explicitly state that the tool is read-only, but it is implied. Rate limits are not mentioned, but overall the description covers many behavioral aspects beyond the minimal.

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 concise, using a single paragraph with clear bullet points. It front-loads the purpose, includes all essential information without redundancy, and earns each sentence's place. 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 the tool's complexity (11 parameters) and lack of annotations, the description covers all critical usage aspects: required vs optional, parameter interactions, pagination, cost, and output usage. Since output schema exists, return values need not be detailed further. The description is complete for correct 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 coverage is 100%, so baseline is 3. The description adds value by explaining default radius in relation to sort_by, and specifying that when sort_by is Distance, radius should be omitted and keyword or place_type provided. It also explains cursor usage, which is not fully covered in 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 searches for places near a latitude and longitude. It uses a specific verb ('Search') and resource ('places near a location'), and distinguishes itself from sibling tools like google-maps.search and place details endpoints.

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 explains required and optional parameters, default radius behavior based on sort_by, and pagination using cursor. It also advises when to omit radius and provides guidance on using place_id with other endpoints. This helps the agent decide when to use this tool and how to use it correctly.

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 the full burden. It discloses that the return is a flat place object, lists common fields, notes that additional upstream fields may appear, and states the token cost (4 tokens). This is sufficient for a read-only 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 five sentences, front-loaded with the core purpose. Every sentence adds useful information: purpose, input source, optional params, return details, and cost. No redundancy or 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?

Given that an output schema exists (context signals indicate true), the description does not need to fully detail return structure. It lists typical fields and warns of additional upstream fields. Cost is included. Could mention location format (lat/lng) but not essential.

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 detailed descriptions for all three parameters. The description adds minimal value beyond the schema: it reiterates that place_id comes from search results and mentions the alternative business identifier form. Since schema already does the heavy lifting, a 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 explicitly states the action ('Get detailed information') and the resource ('a Google Maps place'). It distinguishes from sibling tools like search or nearby_search which return lists, while this retrieves details for a specific place using a place_id.

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 instructs to 'Provide place_id from search results', implying this tool is used after obtaining an identifier from a search. It does not explicitly state when not to use it versus other place-related siblings (place_photos, place_reviews), 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.

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

With no annotations, the description carries the full burden. It discloses the return format (photos array with photo_url and description), pagination via cursor_next, and cost of 3 tokens. No contradictory behavior noted.

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 concise, front-loaded with the purpose, then provides parameter guidance, return details, and cost in a clear, structured manner with no redundant 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 tool has 4 parameters and an output schema, the description covers input requirements, pagination, and return structure adequately. It is sufficient for an agent to understand and invoke the tool, though it lacks error handling details.

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%, so baseline 3. The description adds value by explaining the place_id can be in business identifier form, cursor usage from previous response, and region/language effects, going beyond the schema's minimal 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 explicitly states 'Get photos for a Google Maps place' with a specific verb and resource. It clearly distinguishes the tool from siblings like place_details and place_reviews by focusing on photos.

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?

Provides guidance on providing place_id from search results and pagination cursor usage, but does not explicitly state when to use this tool over alternatives like place_details or place_reviews.

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 pagination via cursor_next, cost of 3 tokens, and the structure of returned data (place metadata, reviews array, fields). It also notes that additional upstream fields may appear. No contradictions or omissions are evident.

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-organized into short paragraphs: purpose, required parameter, optional parameters with usage, output structure, and cost. 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 the tool's moderate complexity and the presence of an output schema, the description adequately explains the return format and pagination. It could mention error handling or rate limits, but the core functionality is well covered.

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% coverage with descriptions for all 5 parameters. The description adds minimal extra value by enumerating sort_by options and clarifying cursor usage for pagination. For high schema coverage, 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 starts with 'Get reviews for a Google Maps place', clearly stating the verb and resource. It distinguishes itself from sibling tools like place_details (general info) and review_details (single review) by focusing specifically on reviews.

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 explains that place_id comes from search results and that the business identifier form is accepted. It lists optional parameters and pagination usage. However, it does not explicitly state when not to use this tool versus alternatives like review_details or place_details.

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?

Discloses that the tool returns a string array and costs 1 token. No annotations are present, but the description is clear about the output and cost. No edge cases or limitations are mentioned, but the tool is simple and non-destructive.

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?

Extremely concise with three sentences: purpose, return/usage, and cost. No unnecessary words, front-loaded with the main action. Every sentence adds value.

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 has an output schema, the description completes the picture by explaining the purpose, usage context (how to apply values), and cost. It is fully informative for a simple list 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?

The tool has 0 parameters, so baseline score of 4 applies. The description does not need to add parameter-specific information as there are none.

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 it lists supported Google Maps place type values for search filters. It specifies the return type (string array) and distinguishes it from other tools by indicating its use as a reference for place_type parameters in google-maps.search and google-maps.nearby_search.

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?

Explicitly instructs to use the returned values with place_type on google-maps.search or google-maps.nearby_search. Also mentions cost. Does not include when-not-to-use, but that is not necessary for a simple reference tool.

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 carries the burden. It mentions the return structure and cost but doesn't discuss permissions, safety, or error behavior. It's adequate but not thorough.

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 concise with three sentences covering purpose, usage, and output. It is front-loaded with the main 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?

Given the presence of an output schema, the description adequately covers the tool's purpose, input sourcing, and typical output fields. It could mention error handling or prerequisites, but it's reasonably complete.

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 single parameter review_id is described in the schema. The description adds value by specifying how to obtain its value (from a place reviews response), going beyond the schema.

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 gets details for one Google Maps review, using a specific verb and resource. It distinguishes from siblings like place_reviews by specifying the source of the review_id.

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 indicates to provide review_id from a place reviews response, giving clear usage context. It doesn't explicitly exclude other scenarios, but the sibling place_reviews is implied for listing.

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 full burden. It discloses the output fields, pagination behavior ('cursor_next and cursor_previous appear only when pagination cursors are available'), and cost (10 tokens). It also notes that 'additional upstream fields may appear,' which is transparent about potential extra data.

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 concise and front-loaded with the primary action. Every sentence adds value: optional filters, default radius, pagination, return fields, chaining, cursor behavior, and cost. No unnecessary repetition or 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?

Given the tool's complexity (10 params, 1 required, has output schema), the description covers purpose, parameters with defaults, return structure, pagination, and cost. It also mentions that additional upstream fields may appear. This is sufficient for an agent to use the tool correctly without additional information.

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%, so baseline is 3. The description adds minor context like default radius (1000 meters) and cursor usage, but largely repeats schema descriptions (e.g., location override comment). No significant new parameter meaning beyond what schema provides.

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 'Search Google Maps by text query.' It distinguishes from sibling tools like place_details, place_photos, and nearby_search by specifying that it returns matching places with key fields (names, full_address, place_id, etc.) and that results can be used with other endpoints via place_id.

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 on when to use this tool (text-based search) and how to handle pagination (using cursor). It also suggests chaining with place detail, review, and photo endpoints. However, it does not explicitly contrast with nearby_search or discuss when not to use this tool.

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 transparency burden. It mentions 'Cost = 5 tokens' and describes the return type, but does not explicitly state if the operation is read-only, idempotent, or any side effects. The cost disclosure is a positive but incomplete.

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 sentences, front-loaded with the primary purpose. Every sentence adds necessary information: purpose, output, and cost. 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 the tool's simplicity (one required param, no nested objects) and the presence of an output schema that likely details the return structure, the description provides sufficient context. It covers the core functionality, input constraints, and cost.

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% for the single parameter, which already describes 'Partial search keywords or phrase.' The description adds value by explaining the return value (normalized query and suggestions) and cost, going beyond the schema. However, it does not add new parameter-level detail.

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 'Get', the resource 'Google Search autocomplete suggestions', and the input 'partial query'. It also mentions the output (normalized query and array of suggested phrases). This differentiates it from siblings like google-search.keyword_traffic_insights or google-search.languages.

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 for partial queries ('autocomplete suggestions for a partial query') but does not explicitly state when to use or when not to use this tool versus alternatives. No exclusions or alternatives 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?

No annotations provided, so the description carries the full burden. It discloses the output structure and cost (20 tokens), but does not explicitly state that the tool is read-only, safe, or if authentication is required. It partially covers behavioral traits but is not comprehensive.

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 concise (two paragraphs) and front-loads the purpose and output structure. Every sentence adds value without redundancy. It could be slightly shorter but is well-structured.

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?

With an output schema present, the description does not need to detail return values. It covers purpose, parameter list, cost, and output structure sufficiently for a keyword traffic insights tool. Slight gaps (error handling) are acceptable given the 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%, so baseline is 3. The description adds some value by summarizing parameters with examples (en, US) and clarifying default behavior (omit location for global), but much of this is already in the schema descriptions. It does not significantly extend beyond the schema.

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 ('Get') and identifies the resource ('Google keyword traffic insights and related keyword suggestions'). It clearly distinguishes from sibling tools like google-search.autocomplete and google-search.url_traffic_insights by focusing on keyword traffic, not autocomplete or URL metrics.

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 states required and optional parameters, defaults, and cost. It implies usage context (keyword research) but does not explicitly mention when not to use this tool (e.g., for URL traffic use url_traffic_insights). This is 'clear context, no exclusions' matching a 4.

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, but description covers output format, example values, and cost. Read-only nature is implied.

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?

Three sentences, each adding value: purpose, output details, and cost. No 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?

Fully describes the tool's purpose, output, and usage context. No gaps given the simplicity.

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?

No parameters, so schema coverage is 100%. Description adds context about usage with other tools and output format, meeting baseline for zero-parameter tools.

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?

Clearly states the tool lists languages for use in google-search.keyword_traffic_insights and url_traffic_insights. Distinguishes from siblings by specifying its role as a supporting tool.

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?

Describes when to use (to get language parameters for other tools) and mentions no request parameters. Lacks explicit exclusion of alternatives like google-search.locations.

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, but description discloses it has no request parameters, returns an array with specific fields, and has a cost of 5 tokens. This fully covers the read-only, deterministic 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?

Three concise sentences: purpose, output format, cost. No redundant phrases. Front-loaded with key action and target tools.

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?

Tool is simple with no parameters and output schema exists. Description explains output structure and purpose completely, leaving no gaps for an agent to use 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?

No parameters exist, so baseline 4 applies. Description adds no parameter info, but none needed.

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?

Description clearly states the tool lists countries and region codes, with specific verb 'List'. It distinguishes from sibling tools like seo.country_codes by linking exclusively to google-search.keyword_traffic_insights and url_traffic_insights.

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?

Explicitly states when to use: before calling the two Google Search traffic insights tools. Lacks mention of alternatives like seo.country_codes but provides sufficient context for an agent to decide.

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 fully bears the transparency burden. It discloses the cost (20 tokens), the return format (array of keyword suggestions with specific fields), and the behavior (no destructive actions). This is comprehensive for the tool's nature.

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 concise with 4 sentences, each serving a purpose: purpose, return data, parameter details, and cost. It is front-loaded with the main action and efficiently organized.

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?

Despite having 5 parameters and being a complex SEO tool, the description is complete: it explains all parameters, return data, and cost. The output schema exists, so return values are covered. Sibling tools exist but this tool's unique focus on URL traffic insights is clear.

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 covers all 5 parameters, and the description adds meaningful context: it gives an example for language, explains that omitting location yields global results, specifies default min_search_volume as 0, and describes intent filter options. This adds value beyond the schema.

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 gets Google keyword traffic insights and related keyword suggestions for a URL. It distinguishes from sibling tools like google-search.keyword_traffic_insights by focusing on URL-based insights, providing a specific verb+resource combination.

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 explains required and optional parameters, including defaults and examples. It provides context for when to use the tool (for URL keyword insights) but does not explicitly state when not to use it or list alternatives among siblings.

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?

Given no annotations, the description fully discloses that it returns an array of category names and a message, and mentions the cost of 5 tokens. It accurately portrays a simple read-only list operation with no hidden behaviors.

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 four sentences, front-loaded with purpose, includes return values, usage advice, and cost. Every sentence is valuable 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 description is complete for a simple list tool: states purpose, return structure, and usage integration with siblings. The existence of an output schema mitigates the need for further detail on return format. Minor gap: does not mention if the category list is exhaustive or dynamic.

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?

With zero parameters and 100% schema coverage, the description doesn't need to add parameter detail. It adds value by explaining return values and usage context, earning a baseline score of 4.

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 it lists all Google Trends category and subcategory labels, with a specific verb ('list') and resource ('categories'). It distinguishes from sibling tools by noting these labels are passed to other Google Trends tools.

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?

Explicitly advises to use this before interest-over-time or interest-by-region calls when filtering by category, providing clear context and when-not-to-use 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?

Describes the return format (JSON object with keyword keys and location-score mappings) and cost (40 tokens). Without annotations, the description covers basic behavior but lacks details on error handling, rate limits, or permissions, which would enhance transparency.

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?

Extremely concise with three short sentences covering purpose, output, parameters, and cost. No redundant information; every sentence earns its place and is front-loaded.

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 8 parameters and output schema, the description covers the essential behavior, output shape, and parameter defaults/constraints. Minor gaps: no mention of error handling or what happens with invalid inputs, but overall complete enough given the output schema exists.

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%, baseline 3. The description adds value by clarifying defaults (end defaults to now, country defaults to global) and constraints (region requires country, category/gprop default to all) beyond the schema's property descriptions, providing an example for start.

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?

Clearly states 'Fetch Google Trends interest-by-region breakdowns for one to five keywords.' The verb 'Fetch' and specific resource 'interest-by-region breakdowns' with the keyword range distinguish it from sibling tools like interest_over_time or related_queries.

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?

Provides context on when to use (region breakdowns) and explains parameter usage with defaults and constraints (e.g., 'region requires a valid country', 'resolution is COUNTRY (default) or REGION'). However, it does not explicitly mention when not to use or list alternative trend tools.

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?

Despite no annotations, the description discloses the return structure (JSON with keyword-to-timestamp mapping, scores 0–100) and granularity dependence on date range. It also mentions cost and required datetime format, providing good behavioral context beyond basic 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 concise at about six sentences, with no redundant information. It front-loads the main purpose, then briefly explains return format and parameter specifics, 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 seven parameters, full schema coverage, and the existence of an output schema, the description covers the essential aspects: purpose, return format, parameter constraints, and defaults. It is sufficiently complete for a fetch-like tool, though it could mention rate limits or specific error conditions.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by clarifying relationships (e.g., 'region requires a valid country') and defaults ('country defaults to global, category and gprop default to all'), which goes beyond the schema alone.

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 starts with a clear verb and resource: 'Fetch Google Trends interest-over-time series for one to five keywords.' It specifies the exact resource (interest-over-time series) and the number of keywords, distinguishing it from sibling tools like interest_by_region or related_queries.

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 context on required and optional parameters, defaults, and constraints like 'region requires a valid country.' However, it does not explicitly state when to use this tool versus alternatives, nor does it provide guidance on 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 provided, but the description discloses the output structure (geo.countries with label and regions) and the cost (5 tokens). It adds behavioral context beyond the input 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 concise, with three sentences and a cost note. It front-loads the purpose and avoids unnecessary details.

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 is simple (0 parameters, no nested objects). The description sufficiently covers its behavior, output structure, and usage context. The output schema likely provides additional details, but the description is complete for selection and 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?

The tool has no parameters, so the description carries the full burden. It explains the purpose and output, adding meaning beyond the empty schema. Baseline 4 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 tool provides a list of countries and subregions for use in other Google Trends tools. It uses specific verbs (list) and resources (countries and subregions), distinguishing itself from siblings like interest_over_time.

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?

Explicitly advises using this tool before interest-over-time or interest-by-region calls when filtering by geography, and pairs with categories for category filtering. This provides clear when-to-use guidance and alternative tools.

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?

The description fully covers the return format (JSON with top and rising sections, rank index mappings), parameter formats (datetime-with-timezone), defaults, and token cost. No annotations are present, so the description carries the full burden, and it succeeds.

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?

Six sentences, each with clear purpose: purpose, return format, parameter details, references to other tools, and cost. Front-loaded with the main action. No extraneous 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 tool's complexity (7 parameters, output schema present), the description covers all necessary behavioral aspects, parameter constraints, and return structure. It is complete enough for an agent to use 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?

The input schema already has 100% description coverage, so baseline is 3. The description adds value by explaining the required datetime format, dependency between region and country, and default behaviors (gprop, category default to all). This justifies a score above baseline.

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 starts with a specific verb ('Fetch') and resource ('Google Trends related queries') for one to five keywords. This clearly distinguishes it from sibling tools like interest_over_time or interest_by_region.

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 explains parameter defaults and dependencies (e.g., region requires country), and references tools for valid values. However, it does not explicitly state when to use this tool versus other google-trends tools.

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 fully bears the burden of behavioral disclosure. It describes the output structure (array of suggested topics with mid, title, type) and states the token cost (10 tokens). This is sufficient for a read-only suggestion tool, though it lacks potential error cases 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 concise (4 sentences), front-loaded with the purpose, then output details, usage guidance, and cost. Every sentence adds value with 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 only one parameter, no annotations, and an existing output schema, the description adequately covers input requirements, output structure, usage context, and cost. It is complete for the tool's complexity, though it could optionally mention result limits or edge cases.

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% (the keyword parameter has a description). The tool description adds only that it's for a single keyword, which is already implied by the parameter name. Baseline 3 is appropriate since the schema already handles parameter documentation.

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 'Get Google Trends suggestions for a single keyword,' using a specific verb and resource. It also distinguishes from sibling tools by explaining its role in refining keywords before other Google Trends calls like interest-over-time, interest-by-region, or related-queries.

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 'Use this to refine keywords before interest-over-time, interest-by-region, or related-queries calls,' providing clear context for when to use the tool. However, it does not mention when not to use it or list alternatives.

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?

Discloses key behavioral traits: cost is 0 tokens, returns parameter names, defaults, response fields, examples, token cost. No annotations were provided, so description carries full burden; it does so comprehensively.

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, front-loaded with purpose, then usage and cost. No wasted words; highly efficient and clear.

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?

With output schema present, description covers all necessary information: purpose, input, usage context, output content, and cost. Fully complete for the tool's function.

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% and schema already describes the parameter. Description adds examples (website-screenshots.capture) and context for use after list_group_tools, but does not add new semantic meaning beyond 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?

Explicitly states it describes a single Vee3 capability, with clear verb+resource. Distinguishes from sibling meta-tools by specifying it is for a single tool/capability, and references list_group_tools as a preceding step.

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?

Clearly says 'Use this after meta-tools.list_group_tools when you need parameter names, defaults, response fields, examples, and token cost before calling a tool.' Provides explicit context and alternatives (list_group_tools). Also notes cost=0 tokens.

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 carries the burden. It discloses that the tool lists 'live' tools and that the cost is 0 tokens. It doesn't elaborate on what 'live' means or other behavioral aspects, but for a read-only listing, this is sufficient.

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 sentences, front-loaded with the core purpose, includes usage guidance, references a sibling tool, and notes token cost. 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?

For a simple listing tool with no parameters and an existing output schema, the description covers purpose, entry contents, cost, and points to more detailed tool. It is complete.

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 no parameters and schema coverage is 100% trivially. The description adds no parameter-specific information because there are none, so baseline score 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 'List all live Vee3 agent tools' and specifies that each entry includes mcp_tool_name, group_id, and summary. It distinguishes itself from sibling meta-tools.describe by directing users there for schemas.

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 says 'Use this when you need to discover which tools exist and what they are called in MCP' and points to meta-tools.describe for more detail. It could be more explicit about when not to use, but the guidance is clear for a listing tool.

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. The description only mentions 'Cost = 0 tokens', which is helpful but insufficient for full transparency. For a simple list tool with no parameters, the behavioral disclosure is 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 very concise, with three short sentences that are all substantive and front-loaded. 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 the tool has no parameters and an output schema exists, the description covers purpose and usage adequately. It could mention authentication or limitations, but for a simple list tool it is reasonably complete.

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 no parameters, and schema coverage is 100% (vacuously). Baseline score of 4 applies as description does not need to add parameter information.

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 'List all Vee3 capability groups', using a specific verb and resource. It distinguishes from sibling tools like list_all and list_group_tools by focusing on groups.

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: 'Use this to see how tools are organized... before listing tools in a specific group'. It implies when to use and hints at workflow, though lacks 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?

No annotations provided, but the description indicates a read-only operation ('List live tools') and mentions cost is 0 tokens. It does not disclose side effects or permissions, but for a listing tool this is adequate.

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 very concise with three short sentences. The first sentence front-loads the purpose, and 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?

Despite lacking an output schema in the input, the description explains it returns name and summary for each tool. For a simple listing tool, it provides all necessary 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%, and the schema already describes group_id with an example. The description repeats the example but adds no new semantic detail beyond the schema.

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 'List' and the resource 'live tools belonging to a single capability group'. It provides an example group_id and distinguishes from siblings like list_groups and list_all.

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 advises using group_id from meta-tools.list_groups, giving clear prerequisite. It does not explicitly state when not to use or alternatives, but the guidance is sufficient.

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 full burden. It discloses file storage duration (14 days), cost (0 tokens), and return fields. However, it does not mention potential limits on file count or ensure completeness of listing.

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?

Remarkably concise with every sentence providing essential information: purpose, output, usage hint, retention policy, and cost. No wasted words, front-loaded.

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 output schema exists (listing return fields) and no parameters, the description is fully complete. Covers purpose, output, usage, and constraints adequately for a simple list 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?

No parameters exist (0 params, 100% schema coverage). Baseline score of 4 applies as description adds no parameter info but none is needed.

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?

Clearly states verb 'List' and resource 'files currently stored for the authenticated Vee3 account'. Distinguishes from sibling tools like 'upload_file' by focusing on listing existing files.

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?

Explains what it returns and how to use the output (file_name for referencing in other capabilities). Mentions storage duration and cost. Does not explicitly state when not to use, but context is clear.

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 description carries full burden. Mentions cost is 0 tokens, but does not detail side effects (e.g., data storage, response behavior). Adequate but could be more transparent about what happens after submission.

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?

Very concise: three short sentences plus 'Cost = 0 tokens.' Front-loaded with purpose. No unnecessary 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 full schema coverage and existence of output schema, description covers purpose, usage guidance, and key parameters. Does not discuss output format, but that is likely covered by output schema. Complete for a bug report 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 coverage is 100%, so baseline is 3. Description adds minimal value beyond schema: 'Include what you tried, what happened, and any error output' aligns with 'description' parameter but does not elaborate on other 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 'Report a bug, error, or anything that did not work as expected while using Vee3.' It distinguishes from sibling tool 'request_feature' by focusing on failures and inconsistencies.

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?

Explicitly says 'Use this when a capability fails unexpectedly, returns wrong data, or behaves inconsistently.' Also advises to include what was tried and error output. Does not explicitly mention when not to use, but context with sibling request_feature provides implicit alternative.

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 present, so description carries full burden. It mentions 'Cost = 0 tokens' but does not disclose other behavioral traits like async processing, ticket creation, or expected response time.

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 plus a cost note. Front-loaded with purpose. Every sentence adds value, no 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?

Tool is simple (request a feature). Output schema exists, so return values are covered. Description includes what to provide and why. Could mention limitations like rate limits, but overall complete.

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 has 100% coverage with descriptions for all 4 parameters. Description adds minimal extra meaning beyond schema (e.g., reinforces that description should explain why not possible). 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?

Clearly states verb 'Request' and resource 'a new capability or feature that Vee3 does not offer yet'. Distinguishes from sibling meta-tools like list_all, report_bug, etc.

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?

Explicitly says 'Use this when you need something that is not available in the catalog' and provides examples (missing integration, data source, workflow, enhancement). Does not explicitly state when not to use, but context is sufficient.

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 fully covers behavioral traits: it's a search (read-only), returns ranked matches with tool_name, summary, and cost, and notes 'Cost = 0 tokens.' No misleading or omitted 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?

Concise, front-loaded first sentence. Every sentence adds value: purpose, when to use, output, follow-up, optional parameters, cost. 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?

Complete for a search tool with output schema. Covers usage, parameters, output structure, and next steps. No missing information given the tool's 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 coverage is 100%, so baseline 3. Description adds context: explains group_id narrows to capability group, limit defaults to 8 (max 20), and query is keywords or task description. Adds value beyond schema.

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 function: 'Search live Vee3 agent tools by keyword or short task description.' It distinguishes from siblings like meta-tools.describe by advising to call this first when unsure and to use describe on the best match.

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?

Provides clear when-to-use guidance: 'Call this first when you are unsure which tool to use.' Also suggests follow-up with meta-tools.describe. Could explicitly mention when not to use it, but still strong.

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 description carries full burden. It discloses it's a read operation ('get'), cost=0 tokens, and what data is returned. Could mention authentication requirements but it's implicitly assumed for an API token balance endpoint. Still highly transparent.

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?

Three concise sentences: purpose, return fields, usage guidance, cost. 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 no parameters and an existing output schema, description fully covers the tool's functionality and context. Complete for a simple zero-parameter read 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?

No parameters (empty schema, 100% coverage). Baseline 4 as description adds no param info but none needed.

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 'Get the current API token balance' with a specific verb and resource. It distinguishes from sibling tools as no other tool provides token balance. It also lists exact return fields.

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?

Explicit guidance: 'Use this before expensive calls or when you receive a 402 insufficient_tokens response' – tells the agent exactly when to invoke this tool.

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?

Since no annotations are provided, the description fully carries the burden. It discloses that the tool only reserves a slot (not uploads), returns an upload_code, the CLI handles actual upload, files stored for 14 days, billing details, and resolution timeout. No behavioral surprises.

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 lengthy but front-loaded with the core purpose. Each sentence adds necessary information (billing, CLI, storage limits). Minor redundancy could be trimmed, but overall well-structured.

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 multi-step nature (reservation, CLI upload, billing, error handling), the description covers all necessary aspects for successful use. It explains the flow, prerequisites, constraints, and follow-up actions, making it fully self-contained.

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?

With 100% schema coverage, baseline is 3. The description adds value by explaining that the name may omit an extension and that extension is replaced based on detected type, which is not fully clear from the schema alone.

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 starts with 'Reserve a direct upload slot for a local file,' which clearly states the tool's primary verb and resource. It distinguishes itself from the sibling tool 'meta-tools.list_uploaded_files' by mentioning it for follow-up work.

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 explicit step-by-step instructions on how to use the tool: pass file_name, install CLI, run vee3-upload command, use resulting file_name. It also includes troubleshooting for TLS errors and references when to use list_uploaded_files, giving clear 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, the description carries full burden. It discloses read-only behavior and lists returned fields (name, bio, birth date/place, profile image, filmography). Cost is mentioned. No contradictions or hidden 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?

Three sentences: purpose, return fields, cost. Every sentence adds value, no fluff. Front-loaded with the core 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?

Given the simple tool with one parameter and an output schema, the description provides all necessary context: input, output summary, and cost. No gaps for typical usage.

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 covers the single parameter with 100% description coverage. The description re-iterates the slug format example but adds little beyond 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 clearly states the tool looks up a celebrity by slug on Rotten Tomatoes, specifying the resource (celebrity), action (look up), and identifier (slug). It distinguishes from sibling tools like movie_details or search by focusing on celebrity details.

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 for looking up celebrity details by slug but does not explicitly mention when not to use it or compare with alternatives like rotten-tomatoes.search or other celebrity-related tools.

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 description must carry the burden. It discloses the output fields (names, roles, character names, profile links) and cost, but does not mention side effects, authentication, rate limits, or other behavioral traits. For a read-only list tool, this is 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?

Three sentences: purpose, output, cost. Every sentence is necessary, front-loaded with key action, and no fluff. Ideal conciseness.

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 simplicity (one parameter, output schema exists), the description sufficiently covers input and output. Context signals confirm high schema coverage, so no gaps.

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 the parameter already well-described. The description repeats the slug example but adds no new meaning 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 clearly states the verb 'List' and resource 'cast and crew credits for a movie'. It specifies the required input (slug) with a concrete example, distinguishing it from sibling tools like 'movie_details' and 'tv_show_cast_and_crew'.

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 implicitly guides usage by stating it is for movie cast/crew, and siblings include TV show equivalent. However, it lacks explicit when-to-use or when-not-to-use instructions, though the context is clear from the name and examples.

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 description bears full burden. States cost (5 tokens) and output fields, but lacks behavioral details like read-only, auth requirements, or rate limits. Adequate but minimal.

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?

Three concise sentences: purpose, return values, cost. Front-loaded with key information, no 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?

For a simple one-param tool with output schema, description covers input, output outline, cost, and purpose sufficiently. No gaps.

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 covers 100% of parameter with description. Description adds example value but does not provide additional meaning beyond schema. Baseline 3.

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?

Description clearly states action (look up), resource (movie on Rotten Tomatoes), and identifier (slug). Lists return fields, differentiating from siblings like cast/crew or reviews.

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?

Mentions input format (slug) with example but does not explicitly guide when to use this vs. search or other movie tools. Implicit from context but no direct guidance.

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, but description discloses pagination mechanism (use cursor from pageInfo.endCursor) and cost (5 tokens). Could mention rate limits or error handling, but for a read tool this is fairly transparent.

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?

Four efficient sentences, front-loaded with purpose and example, followed by return details, pagination, and optional filter. No redundant 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 output schema exists, description adequately covers return fields (review quotes, sentiment, etc.) and pagination. Provides enough context for successful invocation without missing critical details.

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%, so parameters are well-documented. Description adds value with example slug, explicit instruction on cursor usage, and optional type for critic reviews. Reinforces and clarifies schema.

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?

Clearly states it loads reviews for a movie by slug, gives an example, and lists return content (review quotes, sentiment, publication, critic details, pageInfo). Distinguishes from sibling tools like movie_details and movie_cast_and_crew.

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?

Provides instructions on using movie_slug, optional limit, cursor for pagination, and review_type for critic reviews. Does not explicitly state when not to use or compare with alternatives, but usage is clear and straightforward.

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 must disclose behaviors. It states the cost (10 tokens) and lists return fields, but does not address rate limits, authentication, or side effects. This is adequate but not fully transparent.

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 sentences: purpose, return fields, cost. It is concise, front-loaded, and contains no 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 presence of an output schema and simple parameters, the description provides sufficient context about returned fields. However, it omits details like default sorting or result type filtering, which could be useful.

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%, so the baseline is 3. The description adds minor context ('by title or name') but does not enrich parameter meaning beyond what the schema already provides.

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 searches Rotten Tomatoes by title or name and returns movies, TV series, and celebrities. This distinguishes it from sibling detail tools, which focus on specific entities.

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 (e.g., movie_details, celebrity_details). The description does not mention that this tool fetches slugs for subsequent detail lookups.

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 carries full burden. It mentions cost tokens and lists returned fields, but does not disclose any side effects, permissions, or rate limits beyond cost. It is adequate for a read-only operation.

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 very concise with two sentences and a cost note. No extraneous information; essential details are front-loaded.

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 simple nature of the tool (one parameter, output schema exists), the description covers purpose, input example, and return fields. It lacks information on pagination or sorting, but is mostly 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 coverage is 100% with a single parameter described. The description adds no substantial meaning beyond the schema, only reinforcing the example. 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 the verb 'List' and the resource 'cast and crew credits for a TV series by slug', and distinguishes from the sibling tool 'movie_cast_and_crew' by specifying TV series.

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 for TV series but does not explicitly state when to use this tool versus alternatives like movie_cast_and_crew. No exclusion criteria or guidance on alternatives is provided.

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?

The description mentions cost (5 tokens) and lists return fields. Since there are no annotations, the description carries the full burden. It is transparent for a read-only lookup, though it does not mention error handling 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 very concise: two sentences plus cost. No wasted words, purpose front-loaded.

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 output schema, the description is complete enough. It covers what the tool does and what it returns. Lacks mention of errors or edge cases, but acceptable.

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% and the description repeats the slug example provided in the schema. No additional semantic value beyond what the schema offers.

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 purpose: 'Look up a TV series on Rotten Tomatoes by slug'. It also lists the return fields, distinguishing it from sibling tools like tv_show_cast_and_crew, tv_show_episode, etc.

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 for basic TV show details but does not explicitly state when to use this tool versus alternatives like tv_show_cast_and_crew or tv_show_season. No explicit when-not or alternative references.

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 description carries full burden. It lists return fields and token cost but does not disclose error handling, authorization, 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?

Concise three sentences: purpose, return fields, cost. No wasted 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 simple lookup tool with output schema, the description covers purpose, parameters, and returns. Could mention error behavior but overall sufficient.

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%; description largely repeats schema descriptions with minimal added value (e.g., examples already in 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 clearly states the tool loads a single episode given a TV series slug, season number, and episode number. It differentiates from sibling tools like rotten-tomatoes.tv_show_season which loads all episodes of a season.

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 for single episode lookup but does not explicitly state when not to use or provide alternative tools. Sibling tools like tv_show_season are evident but not 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?

No annotations are provided. The description adds cost (5 tokens) and notes Tomatometer scores are returned 'when available', but does not disclose other behavioral traits like auth needs, rate limits, or error conditions. For a read operation, this is adequate but not comprehensive.

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 sentences: purpose, outputs, cost. It is front-loaded, concise, and every sentence provides value with no 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?

With an output schema present, the description appropriately focuses on high-level outputs (season title, episode summaries, Tomatometer scores). It misses handling of pagination or errors, but the tool is simple and the context signals support 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?

The input schema has 100% coverage with clear descriptions, including examples. The description repeats these examples without adding significant new meaning beyond the schema.

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 loads season-level details for a TV series by slug and season number, and lists specific outputs (title, episode summaries, Tomatometer scores). It distinguishes from siblings like tv_show_episode and tv_show_season_reviews.

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 when to use (for season details) but does not explicitly mention when not to use or provide alternatives. Sibling tools exist for other granularities, but no direct guidance is given.

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 description carries full burden. It discloses return fields (quotes, sentiment, publication, critic details, pageInfo), pagination behavior, cost, and optional filter. Lacks mention of rate limits or authentication, but core behavioral traits are well covered.

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?

Four concise sentences, no fluff. Critical info (purpose, pagination, optional filter, cost) is front-loaded and every sentence is meaningful.

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?

Tool has 5 params, required 2, and an output schema. Description covers all key aspects: how to identify the season, pagination, optional filter, and cost. Output schema handles return details, so no missing 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 parameters are documented. The description adds value by explaining how to use cursor for pagination and the effect of review_type. This exceeds schema details.

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 action (Load reviews) and the specific resource (TV season reviews by series slug and season number). It distinguishes from siblings like movie_reviews or tv_show_episode by targeting season-level reviews.

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 explains pagination and optional review_type filter, but does not explicitly guide when to use this tool over alternatives like tv_show_episode for episode reviews or movie_reviews for movies. Context is implied but not explicit.

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 annotation provided, so description carries burden. It discloses the cost (10 tokens) and output structure (overview + list), but no info on side effects, auth, 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 two short paragraphs, front-loaded with the main verb, but the list format could be cleaner.

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 tool with 2 params and an output schema, the description sufficiently covers what the tool returns and its cost.

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% and schema descriptions are clear. Tool description adds no additional meaning beyond stating the parameters exist.

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 finds backlinks for a URL, which distinguishes it from sibling SEO tools that handle metrics, keywords, etc.

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 does not provide any guidance on when to use this tool versus alternatives like seo.basic_metrics or seo.url_metrics.

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 description carries full burden. It discloses cost (10 tokens) and return format, but does not mention side effects, rate limits, or limitations beyond what is implied by the name.

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 plus a cost line, all front-loaded with the main action. No extraneous information; 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?

Given the tool's simplicity (one required param, no annotations, output schema exists), the description specifies return values and intended use adequately, though a note on what it does not cover would be beneficial.

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% and the description does not add meaning beyond what the parameter already conveys. Baseline score of 3 applies as no additional semantic value is provided.

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 retrieves Ahrefs domain authority signals, specifically domainRating and ahRank, distinguishing it from sibling SEO tools like backlinks or keyword metrics.

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 explains it is useful for comparing site strength and prioritizing outreach or competitive research, providing clear context for when to use it, though it does not explicitly exclude alternatives.

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?

Discloses return format (array of ISO codes) and cost (0 tokens). No annotations provided, but the description is transparent about behavior, which is straightforward read-only.

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?

Three concise sentences front-loaded with purpose. Every sentence adds value with no 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 zero-parameter tool, the description covers purpose, output format, and cost. Output schema exists, so return details are adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.