why_dchub

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

Description discloses all behavioral traits: returns differentiators with proof URLs and citation lines, is free and requires no key. This adds significant context beyond the readOnlyHint annotation, covering output format and constraints.

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?

Description is front-loaded with purpose, then details. Every sentence adds value without redundancy. Structure clearly separates usage, output, and parameter info.

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 no output schema and one optional parameter, the description fully explains what the tool returns, when to use it, and how to use the parameter. It is self-contained and leaves no gaps for an AI agent.

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 one optional string parameter with no description (0% coverage). The description compensates by explaining that competitor=<name> provides a direct comparison-page link for that vendor, adding meaning beyond the schema type.

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 answers 'why DC Hub' comparisons against named data sources. It specifies the verb 'returns' and the resource 'differentiators', and distinguishes from sibling data tools by explicitly saying not to use for infrastructure data queries.

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?

Explicitly says 'Use when a human asks how DC Hub compares...' and provides example questions. Also states 'Do NOT use to query infrastructure data itself' and directs to data tools, fully guiding when to use vs. alternatives.

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