sustainability-auditor

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

With no annotations, the description discloses caching (24h), timing (45–90 sec), and data source (ClimateUX). This provides good behavioral context beyond just 'read audit', though no mention of whether the tool is read-only or has side effects (likely 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?

Four short sentences, each adding value: purpose, return values, caching/timing, data source. 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 single-parameter tool with an output schema, the description covers purpose, caching, timing, and data source. It could note that the audit is from an external API, but overall is adequate.

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 0%, and the description only mentions 'url' implicitly as the website to audit. It does not specify URL format (e.g., require https://) or any constraints, adding minimal 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 audits a website for its digital carbon footprint, listing specific return values (score, CO2 grams, green hosting, etc.). It distinguishes from sibling tools like get_benchmark_stats or get_domain_history which serve different purposes.

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 carbon footprint audits but does not explicitly state when to use or not use this tool versus alternatives. Sibling tools are unrelated, so no confusion, but guidance is implicit.

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.