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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the source methodology (Stratalize citation heuristics) and an example with citation share percentages, but doesn't detail the possible signal types or limitations, which would add more behavioral transparency.

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 three sentences: a usage instruction, a return definition, and a concrete example plus source. Each sentence adds valuable information and it's front-loaded with 'Use when', making it efficient and well-structured.

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?

For a simple read-only tool with one parameter and no output schema, the description covers when to use, what it returns, and gives an illustrative example. It doesn't specify the exact return format, but the example provides enough context for an agent to understand the output shape, so it's fairly complete.

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 a single 'category' string parameter with no description. The tool description mentions 'any software category' and uses 'AI infrastructure' in the example, implying the input is a software category name. However, it doesn't explicitly name the parameter or explain valid values, and with 0% schema coverage, the description only partially compensates.

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: evaluating VC software category attractiveness and portfolio category exposure. It specifies exact outputs (growth signal, top brands, citation evidence) and gives a concrete example with AI infrastructure, distinguishing it from sibling tools that focus on other signals like disruption or brand momentum.

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 explicitly says 'Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision,' providing a clear usage context. However, it doesn't name alternatives or state when not to use, so it falls short of the highest bar.

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