Compare SinoGate models

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

With no annotations, the description carries the full burden, and it does disclose parallel fan-out, the per-model return payload (answer, latency, token usage), and that calls are billed to the configured account. It omits important behavior: what happens when one of the models errors or times out, whether partial results are returned, and any rate or cost limits.

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 action and the range constraint, then the return values and billing note. Nothing is padded, though the billing clause is the only sentence carrying non-obvious information.

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 parallel fan-out tool with no output schema, describing the per-model return shape is appropriate, but the definition is silent on failure/partial-result semantics and on three of five parameters. Enough to attempt a call, not enough to predict edge-case behavior or cost.

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 only 40%, and the description adds meaning only for the two documented params, restating the 2-to-4 model constraint and the unchanged prompt. The three undocumented params (thinking, max_tokens, temperature) get no explanation of defaults or whether they are applied uniformly to every model in the comparison.

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 names a specific verb and resource ('Run the same prompt against 2 to 4 SinoGate model IDs in parallel') and states the outputs, which clearly separates it from a single-shot chat tool. It never names sinogate_chat or sinogate_list_models explicitly, so the sibling differentiation is implicit rather than stated.

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 parallel-multi-model framing implies a comparison scenario, but the description never says when to prefer this over sinogate_chat with a single model, nor when to avoid it (e.g. cost-sensitive or latency-sensitive calls). Usage is inferable but not spelled out.

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