Human4AI

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond annotations: no authentication required, the case is versioned, and the returned content includes candidate, boundary, and test prompts. 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, tightly written sentence with no filler. The authentication fact is front-loaded, and every remaining word contributes to specifying the operation and expected content.

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 one-parameter read tool, the description covers auth, operation, and payload composition. However, 'versioned' is ambiguous—whether it returns one version or a versioned document—and slug semantics are absent. Since there is no output schema, these gaps leave some uncertainty.

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 only parameter, slug, is not explained in the description. The description does not state that slug identifies the case or how versioning affects the parameter, so it fails to compensate for the schema's lack of documentation.

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?

States a specific verb ('Read'), a precise resource ('one versioned synthetic case'), and the payload contents ('candidate, boundary and test prompts'). The singular focus clearly distinguishes it from sibling list tools like list_public_cases.

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?

Provides useful context that no authentication is needed and that this retrieves a single case, but it never explicitly explains when to choose this over list_public_cases or list_public_feedback. The usage is implied by the singular/plural contrast rather than stated.

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?

The annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond that, including that no authentication is needed and that the data is synthetic and not private or policy-verified.

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 short sentences with no filler. The no-auth requirement is front-loaded, and the data-scope disclaimer adds useful clarification without bloating the description.

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 simple public read-only listing tool, the description covers access and data nature well. However, because there is no output schema and the description does not mention pagination or return-value shape, an agent may still be uncertain about cursor/limit semantics and what a case entry looks like.

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 for undocumented parameters. It implies q is a search query supporting Chinese or English, but it says nothing about limit or cursor behavior, leaving two of three parameters underspecified.

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 a specific action ('Search') and resource ('synthetic customer-support cases'), and notes the language scope. It also explicitly says the data is not private or verified, which distinguishes this public-cases tool from anything policy- or privacy-related.

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 searching public synthetic support cases with no authentication. It does not explicitly name alternatives like get_public_case or list_public_feedback, but the 'Not private customer data or verified policies' phrasing helps an agent avoid misuse.

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 indicate readOnlyHint and non-destructive behavior, and the description adds valuable context beyond that: no authentication needed, content is untrusted and unverified, pending/retracted content is excluded, and it includes discussions/replies/observations. This clearly sets user expectations about data quality and availability.

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?

Three concise sentences deliver essential information with no filler. The no-authentication note is front-loaded, followed by content type and exclusions. Every sentence adds meaningful value.

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 purpose, authentication, content trust, and filtering exclusions, which is strong for a read-only list tool. However, with no output schema and no explanation of pagination-related parameters (limit, cursor), the agent is left without explicit guidance on handling large result sets or iterating through pages.

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% for the three parameters (slug, limit, cursor), and the description provides no information about their meaning, constraints, or how they affect the request. The description does not compensate for the lack of schema-level parameter documentation.

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 identifies the operation ('Read'), the resource ('owner-published discussions, replies and self-reported observations'), and adds scope boundaries (public, pending/retracted excluded). It distinguishes itself from the sibling case-related tools by focusing on feedback content specifically.

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 reading public feedback and notes no authentication is required, but it does not explicitly mention when to prefer this tool over the sibling tools list_public_cases or get_public_case. The differentiation is implicit through the resource type rather than stated.

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