sustainability-auditor

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it returns specific metrics (score, CO2, hosting status, etc.), mentions caching ('Results cached 24h'), performance ('~45-60 seconds'), and data source ('ClimateUX'). This covers operational aspects like timing and data freshness beyond basic functionality.

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

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

The description is appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: the first states what it does, the second lists outputs, and the third covers behavioral details (caching, timing, source). There is no wasted text, 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 tool's complexity (involving external data and performance considerations) and the presence of an output schema (which handles return values), the description is complete. It covers purpose, outputs, caching, timing, and data source, addressing key contextual needs without redundancy. No annotations are provided, but the description fills the gap 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 1 parameter with 0% description coverage, so the description must compensate. It implies the parameter is a website URL by context ('Audit a website'), but does not explicitly define 'url' or add format details (e.g., requiring HTTP/HTTPS). Since there's only one parameter, the baseline is high, but some semantic clarification is missing.

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 a specific verb ('Audit') and resource ('a website'), specifying what it measures ('digital carbon footprint'). It distinguishes itself from sibling tools like 'get_benchmark_stats' or 'get_domain_history' by focusing on comprehensive sustainability assessment rather than historical data or statistics retrieval.

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 through the mention of 'digital carbon footprint' and 'sustainability score', suggesting it's for environmental impact analysis. However, it does not explicitly state when to use this tool versus alternatives like 'get_benchmark_stats' (which might provide related metrics) or provide exclusions (e.g., not for non-web resources).

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 adds context by specifying the data source ('ClimateUX'), scope ('500+ audited websites'), and key metrics included ('average CO2 per page view, average sustainability score, green hosting rate'), though it lacks details on rate limits, response format, or data freshness.

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 in the first sentence, followed by specific metrics and operational details. Every sentence adds essential information (e.g., data source, no API key requirement) with zero waste, making it highly 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 simplicity (0 parameters, no annotations, but with an output schema), the description is mostly complete. It covers purpose, data source, metrics, and access requirements, though it could benefit from mentioning the output format or data update frequency to fully compensate for the lack 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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds value by explaining what data is retrieved (e.g., specific metrics like CO2 per page view) without redundant parameter details, earning a baseline 4 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?

The description clearly states the specific action ('Get aggregate benchmark statistics') and resource ('from ClimateUX's database of 500+ audited websites'), distinguishing it from sibling tools like 'audit_website' or 'get_domain_history' by focusing on aggregated statistics rather than individual audits or historical 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 provides clear context for usage ('No API key required') and implies this is for accessing pre-aggregated benchmark data, but does not explicitly state when to use this tool versus alternatives like 'get_domain_history' or 'audit_website', nor does it mention any exclusions or prerequisites beyond the data source.

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it returns ordered audits (newest first) and a trend classification, and specifies the data source. However, it lacks details on permissions, rate limits, error handling, or pagination (despite a 'limit' parameter), which are important for a read 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 front-loaded with the core purpose, followed by return details and data source. All three sentences add value: the first states the action, the second specifies output ordering and trend, and the third identifies the source. No wasted words or redundancy.

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

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

Given the tool's moderate complexity (2 parameters, no annotations, but has output schema), the description is reasonably complete. It covers purpose, output behavior, and data source. The output schema likely details return values, so the description need not explain them. However, it could better address parameter usage and behavioral constraints like error 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 0%, so the description must compensate. It explains that 'domain' is for retrieving historical data, adding context beyond the schema's generic 'Domain' title. However, it does not clarify the 'limit' parameter's purpose or default behavior. With 2 parameters and partial semantic enhancement, this scores 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 clearly states the specific action ('Get historical carbon audit data and trend'), identifies the resource ('for a domain'), and distinguishes from siblings by focusing on historical data rather than current audits (audit_website), benchmarks (get_benchmark_stats), or configuration (register_api_key). It includes the data source, which further clarifies 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 implies usage for historical data retrieval but does not explicitly state when to use this tool versus alternatives like audit_website (which likely provides current audits) or get_benchmark_stats. No exclusions or prerequisites are mentioned, leaving some ambiguity about tool selection in context.

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

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

With no annotations provided, the description carries full burden and does well: it discloses the free tier limitations (10 audits/month, no credit card), idempotent behavior (same email returns same key), and implies this is a registration/write operation. It doesn't mention rate limits, authentication needs, or error conditions, but provides solid behavioral context for a registration 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 extremely concise (two sentences) with zero waste. The first sentence states purpose and key constraints, the second explains idempotency. Every word earns its place, and information is front-loaded appropriately for a simple registration tool.

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 this is a registration tool with 2 parameters, 0% schema coverage, no annotations, but with an output schema (which handles return values), the description provides good context. It covers purpose, limitations, and idempotency. For a simple registration endpoint, this is reasonably complete, though it could benefit from mentioning what the API key enables or error 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 0%, so the description must compensate. It mentions 'email' as the key parameter for registration and idempotency, which aligns with the required 'email' parameter in the schema. However, it doesn't explain the optional 'name' parameter or provide format/validation details for either parameter. The description adds some meaning but doesn't fully compensate for the schema coverage gap.

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: 'Register for a free ClimateUX API key' with specific details about the key's limitations (10 audits/month, no credit card). It distinguishes from siblings like 'audit_website' or 'get_benchmark_stats' by focusing on account registration rather than data retrieval. However, it doesn't explicitly contrast with siblings beyond the different domain of operation.

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

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

The description implies usage context: 'Register for a free ClimateUX API key' suggests this should be used when a user needs an API key for the service. It mentions idempotency which helps understand when repeated calls are safe. However, there's no explicit guidance on when to use this versus alternatives (though siblings are unrelated) or any prerequisites beyond providing an email.

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