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

Annotations already declare readOnlyHint and openWorldHint. Description adds important behavioral details: each topic is looked up on its own scale (non-comparable), independent fetching per section, and normal partial failures. Does not contradict annotations.

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 short paragraphs: first explains capability and structure, second gives usage guidance. No wasted words. Front-loaded with the most critical info (max 10 topics, same full picture).

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?

Given the tool has only 2 parameters (both well-documented in schema) and no output schema, the description sufficiently covers behavior, usage context, error handling, and comparability caveat. A partial result note substitutes for return format details.

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 100%, so baseline is 3. Description reinforces parameter semantics: 'topics' must be 1-10 items, each looked up independently and not comparable across topics. Adds clarity that topics can be terms or topic IDs, which is not in the schema.

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 the tool researches up to 10 topics per call, giving the same full picture as research_trend (interest over time, regional distribution, related queries). It clearly distinguishes this bulk version from the single-topic sibling by specifying batch size and mention of 'full picture' equivalence.

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?

Explicitly advises when to use: 'when you need data across many topics — a long or rich research pass — instead of one tool call per topic.' Also explains partial results are normal, setting expectations for error handling.

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