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

Beyond the annotations, the description discloses important operational behavior: it chains multiple upstream tools, costs 7 credits with a bundle discount, and returns a quality object that tells the agent whether the result is shippable or only a starting point. This gives the agent useful expectations about side effects, cost, and reliability without contradicting the annotations.

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 long but every clause earns its place: use case, deliverable composition, chained workflow, cost, output shape, and quality caveat are all present. The most important information is front-loaded, and the output semantics are delivered as a structured, skimmable summary.

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 7 parameters, rich schema descriptions, an output schema, and a complex composite workflow, the description still covers the core deliverables, the return object, and how to interpret partial or non-GO results. It provides enough context for an agent to invoke the tool correctly and know what to do with the result.

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%, and the parameter descriptions already explain each field, including defaults, enums, and behavioral effects like deadline_hours affecting trend-still-alive scoring. The main description does not add much new parameter-level meaning beyond naming product and niche context, so the baseline of 3 is appropriate.

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 names a specific verb and resource — turning a brand brief into a one-page creative blueprint — and enumerates the exact components of that blueprint (hook, template, hashtag combo, trend-velocity check, shoot instructions). It also distinguishes itself from the sibling chain tools by explaining that it bundles find_viral_template, trend_pulse, score_hook, and predict_virality into a single MCP tool.

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?

It gives a concrete triggering scenario: a brand sends a product and needs content on a tight turnaround with little research time. It also clarifies the alternative — stitching the upstream tools manually — and why this tool exists instead, making the when-and-why decision explicit.

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