search_gov_policy

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

With no annotations, the description carries the full burden, and it is transparent: it discloses that 'all' includes a Bing fallback, that sources are official interfaces including document number and validity metadata, and it specifies the exact return shape. No behavioral traits are hidden.

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 front-loaded with a one-sentence summary, followed by a compact Args block and a Returns block. Every line adds relevant information—examples, source meanings, and output fields—so nothing is wasted.

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 three-parameter tool with no output schema and no annotations, the description is complete. It defines all parameters, the available scopes, and the exact result structure, so an agent can call it accurately without additional information.

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, and it does thoroughly. Each parameter is explained: keyword with concrete examples, scope with the full enumeration of options and their meanings, and limit with an explicit upper bound (<=10).

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 states a clear verb-resource pair: '检索政策/法规/案例' (search policies/regulations/cases) across '权威渠道' (authoritative channels). The detailed scope list and the return fields make it unambiguous what the tool does, and the sibling tools are all different operations (fetch, verify, batch-verify, freshness, catalog), so it is clearly the search tool among them.

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 provides rich context on when to use the tool by enumerating the scope values and their meanings, such as 'state_council' for the Chinese government policy database and 'all' for a broader search with a Bing fallback. However, it does not explicitly state exclusions or direct alternatives (e.g., when to prefer verify_law_article_tool), so a point is withheld.

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