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

Annotations already mark the tool as readOnlyHint=true and destructiveHint=false, so the description does not need to repeat safety. It adds useful context: the source (Stratalize AI citation index), pricing ($0.10 USDC per call), and the methodology (unprompted AI mention frequency across 100 queries). However, it does not disclose response format, pagination, or edge-case behavior (e.g., empty categories), but that is not critical given the simple read-only nature.

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 well-structured: it starts with the usage scenario, then states the return value, provides a concrete example, and ends with source and cost. Each sentence adds value, with no fluff. The length is justified given the need to convey the methodology and pricing.

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 one-parameter read-only tool with no output schema, the description is fairly complete: it explains the purpose, gives an example of the output, and notes the cost. It does not describe the exact return structure (array vs. object), but the example implies a ranking list, which is sufficient for most agents to infer the expected output.

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 provides only the parameter name 'category' with no description (0% coverage). The description compensates by giving an example ('CRM category') and implies the parameter is a software category name. However, it does not specify allowed formats, case sensitivity, or if there are standard canonical names. It adds partial meaning but leaves some ambiguity.

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: to return vendors ranked by AI mention frequency in a software category. It uses a specific verb ('Returns') and resource ('vendors ranked by unprompted AI mention frequency'), and the example with Salesforce, HubSpot, and Microsoft Dynamics makes the exact deliverable unmistakable, distinguishing it from generic vendor tools like get_top_vendors_by_category.

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 when to use it ('Use when assessing brand visibility in AI-generated recommendations or researching which vendors dominate AI platform responses'). It provides a clear context but does not mention when not to use it or name alternatives. The adjacent get_top_vendors_by_category or get_ai_consensus_on_topic could be similar, but the description's specificity is adequate for most agents.

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