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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe read-only external access. The description adds that results are raw clusters requiring LLM review and summarization, which is useful behavioral context. However, it does not disclose limitations like potential noise, data source specifics, or rate limits. Given annotations cover safety, this is acceptable but not rich.

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 sentence that front-loads the key differentiator (no keywords needed) and states the core function. It is efficient and avoids redundancy, though it could be slightly more structured with a clearer separation of method and output expectation.

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 tool with two optional parameters and no output schema, the description explains the method (title similarity clustering) and notes the output requires LLM review, which gives the agent an idea of what to expect. However, it does not describe the format or granularity of the returned topics, nor does it mention any integration with siblings like cross_platform_overlap. This is adequate but not 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?

Schema description coverage is 100%, so both parameters (platforms and min_platforms) are already documented in the schema. The description does not add extra meaning beyond implying multi-platform aggregation. Since the schema handles parameter documentation, a baseline of 3 is appropriate; the description adds no further value 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?

The description clearly states the tool's purpose: automatically cluster and discover topics resonating across multiple platforms without requiring keywords, using title similarity. It also notes the output requires LLM review. It distinguishes from keyword-based tools like keyword_trend_curve by emphasizing '无需关键词', but does not explicitly differentiate from cross_platform_overlap which likely overlaps in function.

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 implies the tool is used when no specific keywords are available, suggesting it is for broad discovery rather than targeted queries. However, it does not explicitly name alternative tools or provide conditions for when to choose this over cross_platform_overlap or get_trending. The guidance is implied rather than explicit.

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