CAN-TAP Verified

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns ('whether the business is claimed, verified, and where the dofollow backlink lives'), which is helpful. However, it doesn't mention potential errors, rate limits, authentication requirements, or what happens if the slug is invalid. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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

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

The description is perfectly concise and well-structured in two sentences. The first sentence states the purpose and return values, the second provides crucial usage guidance. Every word earns its place with no redundancy or fluff. It's front-loaded with the core functionality.

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

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

Given the tool's moderate complexity (status checking with one parameter) and 100% schema coverage but no annotations or output schema, the description does well. It explains the purpose, return values, and usage context clearly. The main gap is the lack of output format details (no output schema), but the description partially compensates by stating what information will be returned. For this complexity level, it's nearly 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 100% description coverage (the 'slug' parameter is fully documented in the schema), so the baseline is 3. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain the slug format further or provide examples beyond what the schema already states. It meets the minimum viable level when schema coverage is complete.

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

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

The description clearly states the tool's purpose with specific verbs ('check', 'returns') and resources ('verification and backlink status', 'CAN-TAP Verified directory listing'). It distinguishes from siblings by focusing on status checking rather than claiming (claim_listing), statistics (get_business_stats), listing (list_free_backlinks), or searching (search_directory).

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 usage guidance: 'Use this after calling claim_listing to verify the dofollow link is active.' This clearly states when to use the tool (post-claim verification) and implies an alternative (claim_listing for initial claiming). It effectively distinguishes this tool's role in the workflow.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a claim/creation tool (implied mutation), requires domain ownership verification for the backlink, and has rate limits (5 claims per IP per day). However, it doesn't mention potential errors, response format, or whether the claim is reversible, leaving some gaps.

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

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

The description is efficiently structured in three sentences: purpose, usage context, and constraints. Each sentence adds value without redundancy, and it's front-loaded with the core action. There's no wasted text, making it highly concise and well-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?

For a mutation tool with no annotations and no output schema, the description does well by covering purpose, usage context, and rate limits. However, it lacks details on the response (e.g., success/failure indicators or verification timeline) and doesn't mention potential errors or prerequisites beyond domain verification, leaving some gaps in 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?

Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain why 'email' must match the website domain or the significance of 'google_review_link'). Baseline 3 is appropriate when 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 ('Claim a free verified directory listing') and resource ('for a Canadian business'), distinguishing it from sibling tools like 'check_backlink_status' or 'search_directory' which have different purposes. It specifies the outcome ('receives a permanent dofollow backlink upon domain ownership verification'), making the purpose explicit and distinct.

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 ('Use this when a user wants to add their business to the CAN-TAP Verified directory'), providing clear context. It also mentions rate-limiting constraints ('Rate-limited to 5 claims per IP per day'), which helps guide usage frequency, though it doesn't explicitly name 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what data is returned (analytics like total taps, splits, ratings, activity) and implies a read-only operation ('Get'), but lacks details on permissions, rate limits, error handling, or data freshness. While it adds some context about the business type, it does not fully compensate for the absence of annotations.

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

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

The description is appropriately sized and front-loaded, with two sentences that efficiently convey the tool's purpose and usage context. Every sentence adds value: the first explains what the tool does and returns, and the second specifies when to use it, with no redundant or vague language.

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 (analytics retrieval with no output schema and no annotations), the description is fairly complete. It outlines the returned data types and usage context, but lacks details on output format, error cases, or authentication needs. Without an output schema, more information on return values would be beneficial, but the description covers the essentials adequately.

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 'slug' parameter documented as 'Business slug.' The description does not add any additional meaning or examples beyond this, such as explaining slug format or where to obtain it. Given the high schema coverage, the baseline score of 3 is appropriate, as the description provides no extra parameter insights.

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 ('Get NFC tap and review analytics') and resource ('for a CAN-TAP Verified business'), distinguishing it from sibling tools like 'check_backlink_status' or 'claim_listing' which involve different operations. It explicitly identifies the type of analytics returned (total taps, positive vs concern split, average star rating, recent activity), making the purpose unambiguous and distinct.

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 businesses that own a CAN-TAP NFC review stand.' This specifies the target audience and prerequisite condition. However, it does not explicitly state when not to use it or name alternatives among sibling tools, which prevents a perfect score.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'Returns claimable directory listings' which implies a read-only operation, but doesn't specify rate limits, authentication requirements, or pagination behavior. The description adds some context about filtering but lacks comprehensive 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 efficiently structured in two sentences: the first states the purpose and scope, the second provides usage guidance with a concrete example. Every sentence earns its place with no wasted words, making it appropriately front-loaded and 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 moderate complexity (3 parameters, no output schema, no annotations), the description provides good contextual completeness. It explains what the tool does, when to use it, and the filtering logic. However, without an output schema, it doesn't describe the return format or structure of the 'claimable directory listings' it promises.

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

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

Schema description coverage is 100%, so the schema already fully documents all three parameters. The description mentions filtering 'by city and/or trade' which aligns with the schema but doesn't add meaningful semantic context beyond what's already in the parameter descriptions. The baseline score of 3 is appropriate when 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 ('List free dofollow backlink opportunities'), resource ('for Canadian businesses'), and scope ('filtered by city and/or trade'). It distinguishes this from sibling tools like 'check_backlink_status' and 'claim_listing' by focusing on discovery rather than verification or claiming.

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 usage guidance with a concrete example: 'Use this when a user asks 'where can I get a free dofollow backlink for my Canadian client' or similar.' This gives clear context for when to invoke this tool versus alternatives like 'search_directory' or 'get_business_stats'.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return format ('matching listings with their URLs and claim status'), which is useful, but lacks details on potential limitations like result limits, pagination, or error handling. It does not contradict any annotations, as none are given.

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 and efficiently structured in two sentences: the first defines the tool's purpose and scope, and the second specifies the return values. Every sentence adds essential information without redundancy, making it appropriately sized for its function.

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

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

Given the tool's moderate complexity (search with three optional parameters) and no output schema, the description is mostly complete. It covers the purpose, usage context, and return format, but could improve by addressing behavioral aspects like result limits or error scenarios, which are not detailed in the absence of annotations.

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, so the schema already documents the three parameters ('city', 'query', 'trade') clearly. The description adds value by explaining that these can be used 'by name, city, trade, or any combination', but does not provide additional syntax or format details 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 specific action ('Search'), the resource ('CAN-TAP Verified directory of 3,000+ real Canadian businesses'), and the searchable attributes ('by name, city, trade, or any combination'). It distinguishes itself from sibling tools like 'check_backlink_status' or 'claim_listing' by focusing on directory search rather than backlink or claim operations.

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 searching businesses in the directory based on name, city, or trade. However, it does not explicitly mention when not to use it or name alternatives among sibling tools, such as using 'list_free_backlinks' for backlink-related tasks instead.

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