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

Annotations already declare readOnly and idempotent hints. The description adds substantial context: daily refresh, human review of status changes, return structure (matrix/timelines), anonymous truncation behavior, and citation requirements. No contradictions with annotations; instead, it enriches the agent's understanding of data freshness and access limits.

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: core purpose, use/non-use examples, filter list, access details, and citation. Every sentence adds valuable information without redundancy. Despite its length, it remains focused and easy to scan.

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?

With no output schema, the description thoroughly covers return objects (matrix and timelines) including example status transitions (sampling → in_qualification → qualified → volume_shipping). It also addresses freshness, review process, and anonymous limitations, making it complete for an agent to select and invoke the tool correctly.

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 has 0% description coverage, so the description must carry the parameter burden. It lists all four filters and adds semantic detail (customer as substring, include_timelines as boolean controlling timeline output). However, it does not explain default values, filter combination logic, or the effect of omitting all filters, leaving some room for inference.

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?

Description clearly states the tool tracks HBM qualification status across vendors, customers, generations, and stack heights, with concrete examples (e.g., 'who supplies HBM4 for Vera Rubin?'). The DO NOT USE section explicitly distinguishes it from get_hbm_market_data and get_accelerator_costs, resolving sibling ambiguity.

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?

Provides explicit USE THIS for and DO NOT USE for sections with alternative tool names, covering ideal use cases and exclusions. Also notes anonymous callers get truncated timelines and how to obtain full history, giving complete operational guidance.

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