Compare Industrial Clusters

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false. The description adds valuable behavioral context beyond annotations: it specifies the return format ('{ count: number, data: [full cluster objects with all fields] }'), error handling ('Returns 400 if more than 10 IDs. Missing IDs are silently skipped'), and constraints ('Max 10 cluster IDs per call'). This enriches the agent's understanding of the tool's behavior.

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 well-structured and front-loaded with the core purpose. Each section (PREREQUISITE, USE WHEN, RETURNS, ERRORS, CONSTRAINT) adds essential information without redundancy. The Chinese translation at the end is concise and does not detract from clarity. Every sentence earns its place by providing actionable guidance.

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's moderate complexity (one parameter, no output schema), the description is complete. It covers purpose, prerequisites, usage context, return format, error handling, constraints, and includes a workflow example. With annotations providing safety and idempotency hints, and the schema fully documenting the parameter, no critical information is missing for effective tool use.

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%, with the parameter 'cluster_ids' fully documented in the schema. The description adds minimal semantic value beyond the schema, only reiterating 'max 10' and noting IDs must come from 'search_clusters'. Since the schema already covers parameter details adequately, the baseline score of 3 is appropriate.

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: 'Compare multiple Chinese apparel industrial clusters side-by-side on key metrics.' It specifies the verb ('compare'), resource ('industrial clusters'), scope ('Chinese apparel'), and distinguishes it from siblings like 'search_clusters' (which finds clusters) or 'analyze_market' (which analyzes markets rather than comparing clusters).

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 explicit guidance: 'USE WHEN user wants to evaluate or choose between 2-10 specific clusters' and 'PREREQUISITE: You MUST first call search_clusters to obtain valid cluster_ids.' It also names the alternative ('search_clusters') and outlines a workflow ('search_clusters → collect cluster_ids → compare_clusters'), clearly differentiating when to use this tool versus others.

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