sustainability-auditor

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses response caching ('Results cached 24h'), execution time ('~45-90 seconds'), and the data source ('ClimateUX'), which are valuable behavioral traits. It does not mention rate limits or error conditions, but the included context is substantive.

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 well-structured: the first sentence states the core purpose, and the following two sentences add needed behavioral and output details. Every sentence earns its place, with no unnecessary fluff or repetition.

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 covers the purpose, the expected outputs (sustainability score, CO2 grams, green hosting, page weight, recommendations), and performance characteristics. With an output schema presumably detailing the return structure, this description is sufficiently complete for a straightforward audit tool. The only minor omission is URL format guidance, but that is not a critical gap.

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 has a single 'url' parameter with no description (0% schema description coverage). The description does not specify URL format expectations (e.g., http:// prefix, full domain, or validation rules), only referring to 'a website'. Since it fails to compensate for the missing schema documentation, the parameter semantics are under-served.

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: 'Audit a website for its digital carbon footprint.' This uses a specific verb and resource, and it distinguishes the tool from siblings 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 provides clear context for when to use the tool: whenever a website's sustainability metrics are needed. It does not explicitly mention alternatives or when-not-to-use, but the distinct purpose makes the usage obvious, so it falls just short of 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, the description must carry the behavioral burden. It discloses that no API key is required and names the data source, which is useful. However, it does not describe potential pitfalls like data freshness, rate limits, or empty responses. 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?

The description is three short, focused sentences. It is front-loaded with the primary purpose and each sentence provides meaningful information (what, includes, auth/source). 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?

Given the tool's simplicity (no parameters, output schema present, no annotations), the description is complete: it specifies the data source, the metrics returned, and the lack of an API key requirement. It sufficiently covers the agent's needs 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 zero parameters, and the schema covers 100% of them trivially. Per rubric, 0 parameters sets a baseline of 4. The description adds no parameter details, but none are 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 the tool's function: 'Get aggregate benchmark statistics' – a specific verb and resource. It distinguishes from siblings like audit_website by specifying 'database of 500+ audited websites' and 'aggregate', implying it is not per-site.

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 gives clear context for when to use the tool (when needing aggregate benchmark stats) and explicitly notes 'No API key required', which is a decisive usage condition. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5.

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 that results are ordered newest first, includes trend values (improving/stable/declining), and states the data source. This goes beyond minimal requirements, though it omits potential edge cases or prerequisites.

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, each providing distinct value: purpose, return details, and data source. It is front-loaded with the primary action, and every word 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 simplicity of the tool (2 params, output schema exists), the description is quite complete. It covers ordering, trend, and data source, while the output schema handles return structure. The main gap is the lack of usage guidance and parameter details, but that does not critically impair 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 0%, and the description does not explain the 'limit' parameter. It only clarifies 'domain' implicitly. The description does not compensate for the lack of schema-level documentation, especially for the limit 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 tool gets historical carbon audit data and trends for a domain. It distinguishes itself from siblings like audit_website by focusing on history and trend, and from get_benchmark_stats by being domain-specific.

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 fetching historical data and trend, but does not explicitly state when to use it over siblings or any exclusions. It lacks explicit alternative guidance, so it is only implied.

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?

Beyond the basic register action, the description discloses idempotency ('same email always returns the same key') and the free audit limit, which are valuable behavioral traits. It does not mention potential side effects like email verification or required existing accounts, but the disclosed traits exceed minimal expectations.

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 sentences, front-loaded with the primary purpose and key facts, with no filler. Every word adds value: the free tier, no credit card requirement, and idempotency behavior.

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 and presence of an output schema, the description adequately covers the essential context: what you get (a key), the cost (free), and a critical behavioral guarantee (idempotent). It does not explain how to use the returned key, but the output schema likely covers return structure, and the registration context 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 has 0% description coverage, so the description must compensate. It only implicitly relates to 'email' via the idempotency statement, and makes no mention of 'name' or email format. This leaves the name parameter completely unexplained and provides minimal added meaning beyond the schema's field names.

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 'Register' and the resource 'ClimateUX API key', specifying what the tool does. It also distinguishes itself from sibling tools like audit_website and get_benchmark_stats by focusing on key registration rather than data 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 gives context (free tier, 10 audits/month) that implies when to use it—before making API calls—but it does not explicitly state when to use this tool versus alternatives. There is no mention of prerequisites or exclusions, though the sibling tools are clearly different in purpose.

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