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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read. The description adds valuable behavioral context: it returns three types of signals, gives an example of the output format, mentions the data source (Stratalize), and notes the cost ($0.10 USDC per call). This goes beyond the annotations and helps the agent set expectations.

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 concise but packed with useful information: use case, outputs, example, source, and cost. It's front-loaded with the use case and returns. The example is illustrative without being verbose. It earns its length by covering essential details in a structured way.

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 there is no output schema, the description provides enough context about the return values via the example and names of the signals. It also mentions the cost and source, which are helpful. For a simple tool with one parameter, it is fairly complete. However, it doesn't specify the exact structure of the output (e.g., whether it's JSON or text), but the example gives a clear sense.

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 input schema has zero description coverage for the 'category' parameter, but the description compensates by stating it applies to 'any software category' and giving a concrete example (CRM). This clarifies the domain and expected input format. It doesn't specify constraints like capitalization or exact naming conventions, but it's sufficient for a single string parameter.

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: assessing SaaS category investment thesis, competitive dynamics, or market momentum. It specifies the exact outputs (growth signal, AI citation leaders, disruption risk) and provides a concrete example (CRM). This distinguishes it from more specialized siblings like get_category_ai_leaders and get_category_disruption_signal by indicating it's a composite.

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 provides a clear when-to-use scenario ('before a strategic decision') and an example. However, it doesn't explicitly mention when to use more specific siblings instead, such as if only AI leaders or disruption signal is needed. It implies a broad use case but lacks explicit exclusions or alternative routing.

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