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

The description substantially enriches the minimal openWorldHint=true annotation by explicitly scoping the 'open world' - detailing exactly which metric types are included (market share, ad investment, reach, awareness) and excluded (satisfaction, complaints, sentiment). This is particularly valuable given openWorldHint signals the tool returns only partial data from a larger set, and the description helps bound expectations.

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?

Well-structured with clear sections: purpose, exclusions, included metrics, excluded categories, and typical queries. Every sentence earns its place - no fluff, all high-information content. The pricing block is properly separated out.

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 simple 2-parameter tool with a defined output schema, the description is exhaustive - it covers the full context an agent needs: what it does, what it doesn't do, what to expect in results (metric categories), and realistic usage patterns. The described parameter formats handle bilingual input cases well.

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 already provides good coverage (100%) with examples for both parameters. Description adds value by showing realistic input patterns ('中国比亚迪股份有限公司市场占有率'; '美国Tesla, Inc.广告营销投入') that demonstrate language mixing (Chinese/English company and country names), which is not obvious from the schema alone.

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?

Clear verb+resource structure: queries periodic changes in user brand awareness by specific enterprise name. Explicitly lists the four metric categories (market share, ad/marketing investment, ad reach, brand awareness) and provides three concrete example query phrasings with real company names. Strong differentiation from the ~130 sibling tools through explicit scope boundaries.

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?

Excellent usage guidance: affirms when to use (querying brand awareness metrics by company) and explicitly states what it is NOT for (customer satisfaction, complaint rates, public opinion/reputation). The 'not included' section (non-category metrics, park/industrial chain filtering) and typical question examples provide clear decision heuristics for an agent.

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