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

No annotations are provided, so the description carries the full burden. It clearly explains the methodology (using historical data, selecting comparison basis by sample sufficiency), and explicitly notes that the reserve price is confidential before opening, but this is a statistical estimate from similar awarded cases. It also clarifies that the analysis varies by tender type (evaluation vs. lowest price). This is rich behavioral context that goes beyond a simple tool 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?

The description is quite long, but it is information-dense and well-structured. It starts with a clear summary, then explains the methodology, the reserve calculation, the comparison basis logic, and ends with usage guidance. It is appropriately front-loaded with the core purpose. The main slight deduction is for being somewhat verbose, but each sentence adds value, so it is not overly wasteful.

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 is comprehensive for a tool with no output schema and no annotations. It specifies inputs (implicitly), explains the algorithmic approach, highlights the key output (reserve distribution), and addresses confidentiality nuances. It even gives the intended use case. There is no missing critical information for an agent to decide whether to invoke it and understand its behavior.

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 0% as per context signals, and the schema only provides field names ('unit_id', 'job_number') with no descriptions. The description does not explicitly explain what each parameter means, but it does reference '案子' (case) and the tool name implies it analyzes a specific case, so likely 'job_number' identifies the case and 'unit_id' the unit. However, the description does not provide explicit mapping, so it is not a perfect score. Given low coverage, the description compensates by giving context that these two parameters are needed for a specific case, but does not detail syntax or format.

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 purpose: analyzing the bottom price of a procurement case and providing three bid suggestions. It uses specific terms like '決標/預算比' and '底價分析' that are distinct from sibling tools. It also explains the output includes a reserve price distribution, which differentiates it from other analytical tools.

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 this tool: when users ask 'this case should bid how much' or 'is it worth bidding'. It implies not for other types of analysis, and differentiates from siblings by focusing on price and reserve, not vendor comparison or committee info.

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