Qualityiris

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

Annotations already declare readOnlyHint: true, idempotentHint: true. The description adds minimal behavioral context beyond 'plain-language product overview.' It doesn't disclose data sources, latency, or return format. With annotations present, this is adequate but not enhanced.

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?

Two sentences, front-loaded with the verb. No wasted words. Every sentence adds value: first states what it does, second gives usage guidance.

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?

No output schema exists, but the description says it returns a 'plain-language product overview.' Given zero parameters and read-only annotations, the description is sufficient for the agent to decide invocation. Could mention return structure but not essential.

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?

There are 0 parameters and schema coverage is 100%. The description doesn't need to add parameter info. Baseline for 0 params is 4, and the description provides no param details, which is fine.

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 states it returns a product overview of Qualityiris, with clear verb 'Returns' and resource. It distinguishes from siblings (get_faq, list_features, list_pricing_plans, submit_interest) by targeting a high-level 'what is' question, not specific details.

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 says 'Use when a user asks 'what is Qualityiris?' or is comparing garment/apparel QA tools.' This gives clear context. It lacks explicit when-not-to-use, but with no parameters and read-only, that's acceptable.

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 and idempotentHint=true, which the description does not repeat. The description adds that it returns 'common evaluation questions and answers,' but does not elaborate on behavior like response format or pagination. Given annotation coverage, this is adequate.

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 that front-load the action and content. Every word is meaningful, with no 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?

The tool has no parameters and no output schema, but the description fully covers what the tool returns (common FAQ answers) and when to use it (product/evaluation questions). This is complete for a simple read-only tool.

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?

There are no parameters (schema coverage 100%), so the description does not need to explain any. Baseline score of 4 is appropriate as there is no parameter information needed beyond 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 specifies that the tool returns common evaluation questions and answers about Qualityiris, covering specific topics like industries, offline behavior, roles, security, AI, and sign-up. This clearly distinguishes it from sibling tools such as list_features or submit_interest.

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 'Use to answer product/evaluation questions,' providing clear context for when to invoke this tool. While it does not mention when not to use or specify alternatives, the sibling list implies differentiation.

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 and idempotentHint=true, so the description's behavioral disclosure is minimal. It adds context about listing modules with descriptions and routes, but no additional behavioral traits (e.g., rate limits, side effects). 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?

A single sentence that conveys purpose, content, and usage context. Every phrase earns its place; 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 zero-parameter, no-output-schema tool, the description fully covers what the tool does, what it returns, and when to use it. The context of siblings and annotations provides enough 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 tool has 0 parameters with 100% schema coverage, baseline is 4. The description adds value by listing the actual modules returned, which is beyond what the empty schema provides. No parameter details 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 uses a specific verb 'Lists' and a clear resource 'core Qualityiris feature modules', explicitly enumerating the modules. It distinguishes itself from siblings like 'about_qualityiris' (general info) and 'get_faq' (FAQ) by focusing on listing feature modules.

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 'Use when a user asks what Qualityiris can do', providing a clear use case with no ambiguity. It implicitly excludes other tools like 'submit_interest' or 'list_pricing_plans'.

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 and idempotentHint. The description adds valuable context about what the pricing includes (no tiers, free accounts for certain roles) and confirms no side effects. It does not contradict 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 extremely concise: two sentences that convey all necessary information about pricing, trial, and free accounts. No wasted words, and the key details are front-loaded.

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 no parameters and no output schema, the description fully explains what the tool returns. It covers the plan cost structure, free trial, and which seats are free. No additional context is needed for an agent to use this tool correctly.

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?

There are no parameters (0 params, schema coverage 100%). With zero parameters, the baseline is 4 per instructions. The description correctly does not attempt to describe non-existent parameters.

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 returns Qualityiris pricing, specifying a single per-inspector plan with exact monthly and yearly costs, a free trial, and notes that certain seats are free. It distinguishes itself from sibling tools like list_features or about_qualityiris by focusing solely on pricing.

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 that this tool is for retrieving pricing information, but does not explicitly state when to use it versus alternatives. However, the content is self-contained and the sibling tools have obviously different purposes, so context is clear.

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 indicate the tool is not read-only and has open-world effects, so mutation is expected. The description adds that it returns a confirmation message and sign-up URL, which is useful behavioral context. No contradictions 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 extremely concise, consisting of just two sentences that front-load the action and output. Every word earns its place with no 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 has 4 parameters, no output schema, and no nested objects, the description adequately covers the purpose, usage triggers, and return value. It could optionally mention validation or rate limits, but overall it is complete enough for an agent to understand and use the tool.

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 already provides 100% description coverage for all 4 parameters. The tool description groups the parameters but does not add new meaning or details beyond what the schema provides, so a baseline score of 3 is appropriate.

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 'Records' and the resource 'Qualityiris', listing the specific fields (name, company, work email, use case). It distinguishes itself from sibling tools like about_qualityiris and list_features, which are informational, by being the only tool that records user interest.

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 the tool: 'Use when a user asks to be contacted, request a demo, or start a trial.' While it does not explicitly mention when not to use or suggest alternatives, the usage context is clear and actionable.

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