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

Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds context about the tool's purpose ('fast agent decisions'), output details (risk, score, blockers, etc.), and mode differences, enhancing understanding without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by key details in a single, efficient sentence. Every phrase adds value, such as distinguishing from siblings and outlining output components, 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 the tool's complexity (brand safety assessment) and lack of output schema, the description adequately covers purpose, usage, and output structure. However, it doesn't detail error handling or exact format of the 'brand safety block', leaving minor gaps. Annotations provide good behavioral coverage, making this mostly 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 description coverage is 100%, so the schema fully documents parameters. The description adds minimal semantics by implying 'mode' affects speed vs. evidence coverage, but doesn't provide extra details beyond what the schema already states (e.g., 'quick' for faster decisions). Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Return only the brand safety block') and resource ('brand safety'), distinguishing it from siblings like 'brandomica_check_all' by focusing on fast decisions and a specific output format. It explicitly mentions using the same pipeline as 'brandomica_check_all' but with a different output scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('for fast agent decisions') and distinguishes it from alternatives by mentioning 'brandomica_check_all' as a sibling with the same pipeline but different output. It implies 'quick' mode is for speed versus 'full' for completeness, though not explicitly naming all 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?

The description adds valuable behavioral context beyond annotations: it explains that checks run concurrently, results are sorted by score descending, and each result includes availability score and safety assessment. Annotations cover safety (readOnlyHint, destructiveHint) and reliability (idempotentHint, openWorldHint), but the description enhances understanding of execution and output format.

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 highly concise and front-loaded: it states the core functionality in the first sentence, adds execution details in the second, and output specifics in the third. Every sentence earns its place 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 the tool's moderate complexity, rich annotations, and no output schema, the description is mostly complete. It covers purpose, behavior, and output content, but could benefit from mentioning error handling or example usage to fully compensate for the missing output schema.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema description coverage, the input schema fully documents both parameters. The description doesn't add any parameter-specific details beyond what's in the schema, so it meets the baseline of 3 without compensating for gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: checking 2-10 brand names in a single call. It specifies the verb ('check'), resource ('brand names'), and scope ('2-10'), distinguishing it from siblings like 'brandomica_check_domains' or 'brandomica_check_social' which focus on specific aspects rather than batch processing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: for batch checking 2-10 brand names. However, it doesn't explicitly state when not to use it or mention alternatives like 'brandomica_check_all' for broader checks or 'brandomica_assess_safety' for safety-only assessments, leaving some guidance gaps.

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?

Annotations already indicate this is a safe, read-only, idempotent, and open-world operation. The description adds valuable context by specifying the report includes 'limitations' and details like 'availability score' and 'trademark filing estimates', which go beyond the annotations to clarify output content and scope.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose, followed by specific components of the report, and ends with the return format. Every sentence adds value without redundancy, making it efficient and 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 tool's complexity (comprehensive reporting) and lack of output schema, the description adequately details the report's components (e.g., 'safety assessment', 'domain costs') and return format ('full JSON report'). However, it could more explicitly address behavioral aspects like rate limits or error handling.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, clearly documenting the 'brand_name' parameter. The description does not add any meaning beyond the schema, such as examples or constraints, so it meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Generate a comprehensive Brand Safety Report') and resource ('with timestamped evidence for due diligence'), distinguishing it from sibling tools like 'brandomica_assess_safety' or 'brandomica_filing_readiness' by emphasizing comprehensive reporting rather than focused checks.

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 due diligence with a comprehensive report, but does not explicitly state when to use this tool versus alternatives like 'brandomica_batch_check' or 'brandomica_check_all'. No exclusions or clear alternatives are 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 adds valuable context beyond annotations: it explains the tool's output format ('structured JSON with a 0-10 availability score and a 0-100 safety assessment') and performance characteristics ('~3-4 API calls' for quick mode). Annotations already indicate it's read-only, non-destructive, idempotent, and open-world, so the description doesn't contradict them but provides additional behavioral details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by output details and usage guidance. Both sentences are essential: the first defines the tool's scope and output, the second explains the mode parameter's practical implications. There is no wasted text, making it highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multiple check types) and lack of output schema, the description does well by specifying the output format (structured JSON with scores). However, it could be more complete by detailing what the '0-10 availability score' and '0-100 safety assessment' mean or listing specific checks included. Annotations cover safety aspects, but the description adds useful context without being exhaustive.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents both parameters. The description adds some semantic context by explaining the 'mode' parameter's impact ('quick' for faster results with fewer checks), but doesn't provide additional meaning beyond what the schema already covers. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check brand name availability across domains (with pricing), social handles, trademarks, app stores, and SaaS channels.' It specifies the verb ('Check') and resources (domains, social handles, trademarks, etc.), and distinguishes it from siblings like 'brandomica_check_domains' or 'brandomica_check_social' by covering multiple aspects in one 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?

The description provides explicit guidance on when to use this tool vs. alternatives: 'Use mode='quick' for faster results with fewer checks (domains without pricing, GitHub only, npm only, trademarks, no app stores or web presence).' It specifies the trade-offs between 'full' and 'quick' modes, helping the agent choose based on speed vs. comprehensiveness.

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?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds useful context by specifying the search scope (iOS App Store and Google Play), which isn't captured in annotations. No contradictions with annotations exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words, front-loading the core action and resources. Every element ('Search iOS App Store and Google Play for apps matching the brand name') directly contributes to understanding the tool's 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?

For a simple search tool with one parameter, full schema coverage, and annotations covering safety and behavior, the description is largely complete. However, without an output schema, it could benefit from hinting at return types (e.g., app listings or matches), though the context is sufficient given the tool's straightforward nature.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the parameter 'brand_name' fully documented in the schema. The description adds no additional parameter details beyond implying it's used for matching in app stores, which aligns with but doesn't extend the schema. Baseline 3 is appropriate given high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Search') and resources ('iOS App Store and Google Play for apps matching the brand name'), distinguishing it from sibling tools like brandomica_check_domains or brandomica_check_social that search different platforms. It precisely communicates the tool's function without redundancy.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when searching app stores for brand-related apps, but lacks explicit guidance on when to use this tool versus alternatives like brandomica_check_all or brandomica_batch_check. It provides basic context but no exclusions or comparisons to sibling 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?

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds value by specifying the scope ('across 6 TLDs') and including 'purchase and renewal pricing,' which are not covered by annotations. It does not contradict annotations, as checking availability aligns with read-only operations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action and key details (6 TLDs, pricing). Every word contributes meaning, with no redundancy or unnecessary elaboration, making it optimally concise and 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 tool's low complexity (1 parameter, no output schema) and rich annotations, the description is adequate but incomplete. It lacks details on output format (e.g., structured pricing data) and does not fully compensate for the missing output schema, though annotations provide good behavioral context.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with a clear parameter description for 'brand_name.' The description does not add any parameter-specific details beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage without extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Check domain availability') and resource ('across 6 TLDs'), with explicit enumeration of the TLDs (.com, .io, .co, .app, .dev, .ai). It distinguishes from sibling tools like 'brandomica_check_all' or 'brandomica_check_social' by focusing solely on domain availability with pricing, making the purpose highly specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention sibling tools like 'brandomica_check_all' (which might check more TLDs) or 'brandomica_batch_check' (for multiple names), nor does it specify prerequisites or exclusions, leaving usage context implied at best.

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?

Annotations already indicate this is a read-only, non-destructive, idempotent, and open-world operation. The description adds value by specifying that it searches Google and detects competitor overlap, which provides context beyond the annotations. However, it does not detail behavioral traits like rate limits, authentication needs, or result format, which would be helpful given the lack of an output 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 and front-loaded, consisting of two sentences that efficiently convey the tool's purpose and key functionality. Every sentence adds value without redundancy, making it easy to understand quickly.

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 (a search operation with one parameter), rich annotations, and 100% schema coverage, the description is mostly complete. It explains the purpose and context well. However, the lack of an output schema means the description could benefit from mentioning what the search returns (e.g., links, summaries), slightly reducing completeness.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'brand_name' parameter well-documented in the schema itself. The description adds minimal semantic context by implying the brand name is used for Google searches, but it does not provide additional details beyond what the schema already covers, such as formatting or usage examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Search Google'), the resource ('existing companies or products'), and the input ('using a brand name'). It distinguishes this tool from siblings by specifying its focus on Google search results and competitor overlap detection, unlike tools like 'brandomica_check_trademarks' or 'brandomica_check_domains'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: to find existing companies or products via Google search, particularly for detecting competitor overlap not in formal registries. However, it does not explicitly state when not to use it or name specific alternatives among the siblings, such as 'brandomica_check_trademarks' for registry-based checks.

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?

Annotations already provide readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds context by specifying the exact platforms checked, which helps the agent understand scope beyond what annotations convey. No contradictions with annotations exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core action and lists all relevant platforms without unnecessary words. Every element (verb, resource, platforms) earns its place by directly informing tool selection and use.

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 low complexity (one parameter, no output schema) and rich annotations covering safety and behavior, the description is reasonably complete. It specifies the platforms checked, which is crucial for contextual understanding. However, it lacks details on output format or potential limitations (e.g., rate limits, error handling), leaving some gaps for the 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%, with the parameter 'brand_name' fully documented in the schema (including type, length constraints, and pattern). The description does not add any additional meaning or details about the parameter beyond what the schema provides, so it meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Check package name availability') and enumerates the exact resources across which this check is performed (npm, PyPI, crates.io, RubyGems, NuGet, Homebrew, Docker Hub, and ProductHunt). This distinguishes it from sibling tools like 'check_domains' or 'check_social' by specifying the package registry and SaaS platform focus.

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 context (checking brand name availability across specific platforms) but does not explicitly state when to use this tool versus alternatives like 'check_all' or 'check_appstores'. No exclusions or prerequisites are mentioned, leaving the agent to infer appropriate scenarios based on the enumerated platforms.

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

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

Annotations already declare this as read-only, open-world, idempotent, and non-destructive, covering key behavioral traits. The description adds value by specifying which platforms are checked (GitHub, Twitter/X, TikTok, LinkedIn, Instagram), which isn't in the annotations. However, it doesn't disclose rate limits, authentication needs, or response format details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function. It's front-loaded with the core action and lists platforms without unnecessary elaboration, making it easy to parse quickly.

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 read-only check tool with good annotations (readOnlyHint, openWorldHint, idempotentHint) and full schema coverage, the description is minimally adequate. However, without an output schema, it doesn't explain what the return value looks like (e.g., availability status per platform), leaving a gap in completeness for agent 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?

Schema description coverage is 100% for the single parameter 'brand_name', with the schema providing format constraints (e.g., pattern, length). The description doesn't add any parameter-specific semantics beyond implying the brand name is used for checking handles. This meets the baseline of 3 when schema coverage is high.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: checking social media handle availability across five specific platforms (GitHub, Twitter/X, TikTok, LinkedIn, Instagram). It uses a specific verb ('check') and resource ('social media handle availability'), but doesn't distinguish itself from sibling tools like 'brandomica_check_all' or 'brandomica_batch_check' which might offer similar functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'brandomica_check_all' (which might check more platforms) or 'brandomica_batch_check' (which might handle multiple names), nor does it specify prerequisites or constraints beyond the implied brand name input.

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?

Annotations already indicate this is a read-only, open-world, idempotent, non-destructive operation. The description adds valuable context beyond this: it specifies which trademark registries are checked (USPTO and EUIPO) and details the technical implementations (Turso SQLite FTS5, Trademark Search API with OAuth2, fallback mechanisms). This enhances transparency about data sources and reliability, though it doesn't cover rate limits or auth needs beyond OAuth2 mention.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the core purpose in the first sentence. The second sentence adds technical implementation details, which are relevant but could be considered slightly dense. Overall, it's efficient with minimal waste, though not perfectly concise.

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 (checking multiple registries with technical fallbacks), annotations cover safety aspects, but there's no output schema. The description provides good context on data sources and implementations but doesn't explain return values or result format. For a read-only query tool, this leaves gaps in understanding what the agent will receive, making it adequate but not fully 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 description coverage is 100%, with the parameter 'brand_name' fully documented in the schema. The description doesn't add any semantic details about the parameter beyond what's in the schema (e.g., it doesn't explain format constraints or provide examples). Baseline 3 is appropriate since the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check trademark registries for existing registrations of a brand name.' This specifies the verb ('check'), resource ('trademark registries'), and target ('brand name'). However, it doesn't explicitly distinguish this tool from siblings like 'brandomica_check_all' or 'brandomica_compare_brands', which might also involve trademark checking, so it's not a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions technical details about providers (USPTO, EUIPO) but doesn't clarify if this is the primary trademark check tool or how it relates to siblings like 'brandomica_check_all' or 'brandomica_batch_check'. There's no explicit when/when-not or alternative 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds valuable behavioral context beyond annotations by specifying what checks are performed (domains, social handles, trademarks, app stores, SaaS channels) and what the tool returns (availability score, safety assessment per candidate, highest-scoring recommendation). This provides important operational details not covered by annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly front-loaded with the core purpose in the first sentence, followed by specific checks and return values. Every sentence earns its place with zero wasted words, making it highly efficient for an AI agent 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 tool's moderate complexity, rich annotations, and 100% schema coverage, the description provides good contextual completeness. It explains what checks are performed and what information is returned. The main gap is the lack of output schema, so the description doesn't detail the structure of the 'availability score' or 'safety assessment' return values.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with a clear parameter description. The description adds some semantic context by mentioning '2-5 brand name candidates' which aligns with the schema's minItems/maxItems constraints, but doesn't provide additional meaning beyond what the schema already documents about the brand_names parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('compare'), the resource ('brand name candidates'), and the scope ('2-5 candidates side-by-side'). It distinguishes from siblings by specifying comprehensive multi-channel checks (domains, social handles, trademarks, app stores, SaaS channels) rather than single-channel checks like 'check_domains' or 'check_social'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: when comparing 2-5 brand names across multiple channels. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (e.g., when you only need to check one channel or want a different type of assessment).

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?

Annotations already provide key behavioral traits (read-only, open-world, idempotent, non-destructive), so the bar is lower. The description adds context about the output structure (e.g., 'decision-focused block' with specific components) and mode differences ('full' vs 'quick'), but does not disclose additional traits like rate limits, auth needs, or data sources. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, dense sentence that efficiently lists all key output components without redundancy. It is front-loaded with the core purpose and wastes no words, making it highly concise and well-structured for quick understanding.

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 (decision-focused readiness assessment) and rich annotations, the description is largely complete. It outlines the output structure in detail, though without an output schema, it could benefit from more specifics on return format. However, it adequately covers purpose and context, balancing well with the provided structured data.

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%, providing clear documentation for both parameters ('brand_name' and 'mode'). The description adds minimal semantic value beyond the schema, mentioning 'check mode' differences but not elaborating on implications. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Return a decision-focused filing readiness block') and resources ('filing readiness block'), listing key components like verdict, filing risk, conflicts, evidence links, confidence, and missing categories. It distinguishes itself from siblings by focusing on a comprehensive readiness summary rather than individual checks or comparisons.

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 decision-making on filing readiness, but does not explicitly state when to use this tool versus alternatives like 'brandomica_check_trademarks' or 'brandomica_compare_brands'. It provides clear context for readiness assessment but lacks explicit exclusions or named alternatives.

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