query_by_quality

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

No annotations are provided, so the description carries the transparency burden. It discloses that the mapping is judgment-based, not factual, and distinguishes auto_approved vs. draft review states. It also clarifies that evidence_quote is a true substring of the original text. Missing details like error handling or authorization, but the core behavioral traits are well covered.

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 a single dense sentence that packs purpose, vocabulary, review states, and defaults without fluff. It is front-loaded with the main action and resource. Slightly long, but each clause carries meaningful 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?

With no output schema, the description partially explains return content by mentioning evidence_quote and review statuses. It covers the key behavioral edge cases (draft filtering, controlled vocabulary). It does not fully specify the return structure or error behavior, but for a simple query tool with three parameters, it is adequately complete.

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 schema has 100% coverage, giving a baseline of 3. The description adds value by providing concrete examples of quality values (忠, 谋略, 勇, 仁, 残暴, 骄), explaining the controlled vocabulary and the review-state semantics for include_draft. This goes beyond the schema's parameter descriptions.

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 tool queries by quality (Chinese: 品质) and returns representative events, people, and stories with original-text evidence. It explicitly enumerates the controlled vocabulary and distinguishes itself from siblings like search_events, get_person, and query_by_place by the query dimension (quality).

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 explains when to use it: when searching by a quality from a 55-term controlled vocabulary, using either Chinese or English slugs. It also specifies default behavior (only machine-approved outputs) and the include_draft option. It does not explicitly state exclusions vs. alternatives, but the context is clear enough.

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