Why DC Hub (vs. the field)

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

Annotations already declare readOnly, idempotent, and destructve hints, and the description adds valuable behavioral context: it returns honest, source-verified differentiators with proof URLs and citation lines, is free with no key required, and even discloses the withdrawn/restored history of the DCGI index. This goes well beyond the structured metadata.

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 dense and front-loaded with the use case, and every sentence contributes useful guidance. However, the first sentence contains a long parenthetical list and the DCGI withdrawal/restoration detail, which could have been structured more cleanly. It is acceptable given the amount of positioning context needed.

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?

The description is complete for this low-complexity, optional-parameter tool. It covers the exact trigger, the return content, exclusions, free/no-key access, and optional parameter behavior. Since an output schema exists, it does not need to elaborate further on return structure.

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 schema has 100% description coverage for the single optional `competitor` parameter, and the description does not add substantial meaning beyond that. It largely restates the schema's existing explanation about returning a direct comparison-page link for a vendor, so the parameter semantics are adequate but not enhanced.

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 identifies the tool's job: answering positioning and comparison questions about DC Hub versus other data-center data sources. It uses a specific trigger ('why should I use DC Hub / is it better than X') and explicitly separates this from infrastructure-data querying with 'Do NOT use to query infrastructure data itself.' This makes it easy to distinguish from the many data-query sibling tools.

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?

It states exactly when to use the tool ('Use when a human asks how DC Hub compares...'), explicitly says when not to use it ('Do NOT use to query infrastructure data itself'), and points to an alternative category ('use the data tools'). Given the large sibling list, the categorical exclusion is practical and actionable.

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