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

With no annotations provided, the description carries the full burden and does disclose the two most material traits: 只读 (read-only, nothing is mutated) and 免鉴权 (no authentication required). It says nothing about return format or rate limits, but for a zero-parameter informational query the safety profile is the key disclosure and it is present.

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?

Front-loaded with a scope marker (【研究层口径】) followed by the query subject, the computed outputs, and example questions. Dense but each clause carries information; the jargon is offset by the concrete example questions.

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-param, no-output-schema informational tool, the description covers purpose, sample prompts, and the read-only/no-auth profile, which is enough to invoke it. The only meaningful omission is how it relates to the sibling explain_evidence_mdv, which an agent would otherwise have to guess.

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 no parameters, so per the baseline there is nothing for the description to disambiguate. No param info is expected or needed here.

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 (查询) and a specific resource (「最小可行同质集」取证判据), and enumerates what it computes (most valuable next data point, zero marginal value of a single declaration, batch ordering). It is clear what the tool does, but it never distinguishes itself from the closely related sibling explain_evidence_mdv.

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 three example questions («取证该从哪开始»/«补机械声明率有没有用»/«K=2 是什么意思») give concrete context for when to reach for this tool. However there are no exclusions and no explicit routing away from the sibling explain_evidence_mdv, so the guidance stops short of full when/when-not coverage.

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