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

Annotations already indicate read-only and non-destructive. The description adds beyond annotations by disclosing the output contents (growth signal, top brands, citation evidence), the source (Stratalize citation heuristics), and pricing ($0.10 USDC per call). The example also reveals the kind of data returned. No behavioral surprises are hidden.

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 compact yet information-dense: use case, returns, example, source, and cost are each covered in a short paragraph. Every sentence adds value; the example is illustrative without being verbose. Slightly longer than necessary, but justified by the need to convey multiple facets.

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 single-parameter read-only tool with no output schema, the description covers the essential context: when to use it, what it returns, an example, the data source, and cost. The main minor gap is no mention of error cases or typical category name formats, but overall it is sufficiently complete for an agent to invoke 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 coverage is 0% and the only parameter 'category' is undocumented. The description compensates partially by saying 'any software category' and given an example ('AI infrastructure category'), clarifying the expected value. However, it does not specify exact naming conventions, allowed formats, or whether partial names work, so some ambiguity remains.

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 states a specific verb and resource: evaluating VC software category attractiveness and portfolio category exposure. It clearly distinguishes itself with outputs like growth signal, top brands, and citation evidence, and provides a concrete example (AI infrastructure with Nvidia, Anthropic, xAI), making its unique purpose obvious among many siblings.

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 evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision,' giving clear when-to-use context. It does not name alternatives or exclusions, but the phrasing implies the specific investment-focused niche, distinguishing it from general category benchmarks or AI leaders tools.

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