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 meaningful behavioral context beyond annotations: it reveals the cost ($0.02 USDC per call), the data source (Stratalize brand index), and the exact output shape with an example. It does not contradict the annotations and provides useful operational details like pricing.

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 front-loaded with the 'Use when' clause and then specifies returns. The example adds clarity and realism, and the source/pricing lines are short. It is slightly verbose with the interpretive phrase 'competitive signal requiring CMO attention,' but overall each sentence earns its place and the structure is logical.

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 the tool is a simple read-only query with one parameter, the description adequately covers return values, source, cost, and an example. It does not explain the interpretation of 'momentum score' or 'trend direction' beyond the example, but that is acceptable for a benchmark-style tool. No output schema exists, so the description handles the return contract well.

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 only defines brand_name as a string with no description (0% schema coverage), so the description must compensate. It gives an example (HubSpot) implying a company/brand name, but does not clarify naming conventions, accepted formats, or whether aliases are allowed. This partial guidance raises it above a 2 but leaves ambiguity for an agent trying to validate inputs.

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: monitoring vendor brand trajectory in AI recommendations and week-over-week competitor momentum. It lists the specific output (4-week momentum score, trend direction, weekly movement series) and provides an illustrative example. This distinguishes it from sibling tools like 'get_competitive_displacement_signal' or 'get_ai_consensus_on_topic' by focusing on brand momentum with a named source.

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 monitoring a vendor brand trajectory in AI recommendations or tracking week-over-week competitor momentum for a CMO brief,' providing clear usage context. It does not mention alternatives or exclusionary criteria, so it falls short of the full 5 for naming when not to use it, but the use cases are specific and actionable.

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