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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is established. The description adds meaningful behavioral context by naming specific data sources (Trends24, RSS feed anomalies) and output types (authenticity scores, growth spikes, flags), which is useful beyond the structured fields. No contradictions with annotations are present.

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 first three sentences are informative and front-loaded. However, the final sentence listing keywords ('influencer marketing, fake follower detection...') is unnecessary filler for an AI agent and does not earn its place. While the description is not bloated, this redundancy prevents a higher score.

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?

With a rich output schema and informative annotations, the description does not need to explain return values in detail. It covers the tool's purpose, method, target audience, and scale ('up to 10'). It could mention limitations or edge cases, but given the structured metadata, it is sufficiently complete for selection and invocation.

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 description coverage is 100%, so the parameters are already well-documented. The description reinforces the 10-influencer limit mentioned in the schema but does not add new semantic details about handle formatting, platform behavior, or async usage. This is acceptable but not additive beyond the baseline.

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 action ('Analyzes'), a precise resource ('up to 10 social media influencers for fake followers'), and the analytical method ('checking engagement velocity patterns and RSS feed anomalies'). It also lists concrete outputs ('authenticity scores, follower growth spikes, and suspicious activity flags'), making the tool's purpose unmistakable and distinguishing it from generic analytics tools.

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 use case: 'Optimized for CMOs evaluating influencer partnerships.' This gives the agent a strong contextual signal for when to invoke the tool. However, it does not explicitly mention alternatives or exclusionary conditions, so it falls short of the highest tier.

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