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

With no annotations, the description carries the full burden and does well: it discloses the non-obvious gateway constraint (强制流式返回) and that the tool aggregates the stream into complete text, plus where reasoning-model thinking lands. It does not mention auth requirements or rate limits, so it stops short of a 5.

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 sentences, zero waste: purpose first, dependency second, non-obvious return-behavior caveat last. Every clause earns its place and nothing is buried.

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?

There is no output schema, so the description must carry return semantics, and it does the essential part by explaining the streamed-then-aggregated response and the reasoning field. Remaining gaps (auth, failure modes) are minor for a straightforward completion call.

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 100%, so the schema already documents all six parameters including the enum and examples. The description only adds the source of model IDs, which is a modest increment over the schema's own example list.

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 (完成一次对话补全) and resource (WorkBuddy AI 国际版任意模型), and explicitly routes the model argument to the sibling wb_ai_models, which distinguishes it from that sibling rather than confusing the agent.

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?

Tells the agent where model IDs come from (wb_ai_models), which is real routing guidance, but offers no explicit when-not-to-use-this or conditions under which the call would be wrong.

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 disclosure burden. It helpfully enumerates what the response contains (credit multiplier, context window, reasoning tier, image support, plus image/video models), but does not state that this is a pure read with no side effects, nor mention caching/freshness behavior (that only appears in the schema's refresh description).

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 sentences, front-loaded with purpose, followed by return contents and downstream linkage. Slightly dense in the second sentence's metadata enumeration, but no wasted text.

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?

With no output schema, the description must convey returns and it does so thoroughly, and it ties the output to wb_ai_chat. It leaves wb_ai_status unaddressed and doesn't restate the read-only nature, minor gaps for a simple one-parameter list 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?

Schema description coverage is 100% and the sole parameter (refresh) is fully documented in the schema, including the 5-minute cache default. The description adds no parameter detail beyond that, so the baseline 3 applies.

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 (列出) and resource (WorkBuddy AI 当前账号可用的全部模型), and even distinguishes its scope from the chat sibling by noting the returned model IDs feed wb_ai_chat's model parameter. An agent can identify this as the model-catalog tool without opening the schema.

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?

Implies usage by stating that returned model IDs can be passed to wb_ai_chat's model parameter, which routes the agent for downstream calls. However, there is no explicit 'call this before chatting' instruction, no exclusion against wb_ai_status, and no stated preconditions.

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?

No annotations are provided, so the description carries the burden; it discloses the important behavioral trait that no model invocation occurs (and thus no credit consumption or side effects), and that local credential/app data is read. It does not spell out auth requirements or side-effect profile beyond that, but for a read-only status probe this is solid disclosure.

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 tight clauses: the first front-loads the purpose and the enumerated data it reports, the second adds the single most important behavioral constraint. No filler, and the crucial restriction is placed last for emphasis.

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?

There is no output schema, and the description explicitly lists the fields an agent will receive (account, token validity, credits, credential file, App version), which is exactly the compensating detail needed. With zero parameters and no annotations, nothing material is missing.

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 takes zero parameters, so the baseline is 4; the schema is empty and there is nothing for the description to compensate for.

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+resource (check WorkBuddy AI connection status) and enumerates exactly what is inspected: login account, token expiry, remaining credits, local credential file, App version. The closing note that it starts no model calls distinguishes it from the model/chat siblings, so an agent can route correctly without opening a schema.

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 this applies (diagnosing connectivity/account state) and an explicit exclusion ('不发起任何模型调用'), steering an agent away from using it as a generation path. It stops short of naming the siblings (wb_ai_models, wb_ai_chat) as alternatives, so it is clear but not fully routing-aware.

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