Analyze Market

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

Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, closed-world), but the description adds valuable context beyond this. It clarifies the tool's role in a workflow ('Standalone analysis tool'), its output scope ('bird's-eye view'), and that it's for market intelligence rather than supplier details. No contradictions with annotations are present.

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 with clear sections (e.g., 'USE WHEN', 'WORKFLOW', 'RETURNS', 'NOTE'), front-loading key information. Every sentence adds value, such as usage scenarios, workflow integration, and output details, with no redundant or verbose content.

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 complexity (market analysis with one parameter) and the absence of an output schema, the description provides comprehensive context. It details the return structure, usage guidelines, workflow integration, and distinctions from sibling tools, making it complete enough for an AI agent to understand and invoke the tool effectively.

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 input schema has 100% description coverage for its single parameter ('product'), so the baseline is 3. The description adds semantic context by specifying the parameter's purpose ('Product category to analyze') and providing examples ('e.g. sportswear, denim, underwear') in the schema, which enhances understanding beyond the basic schema definition.

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: 'Market overview and analysis for a product category in China.' It specifies the verb ('analyze'), resource ('market'), and scope ('product category in China'), distinguishing it from sibling tools like search_suppliers or recommend_suppliers that focus on specific suppliers rather than market intelligence.

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 usage guidelines in the 'USE WHEN' section, listing scenarios like user queries for market overviews or pre-sourcing intelligence. It also distinguishes when to use this tool versus alternatives, explicitly stating to use it 'BEFORE search_suppliers' and noting it's for 'an overview, not specific suppliers,' with clear alternatives named (search_suppliers, recommend_suppliers).

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