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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds behavioral context by stating it requires a Pro plan (authorization constraint) and detailing the analysis process, which goes beyond what annotations provide.

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 three sentences long, front-loaded with the main purpose. Each sentence adds value: core function, analysis details, additional outputs and requirement. No wasted words.

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 presence of output schema, the description adequately covers inputs, outputs (channels, costs, ROI, content ideas), and prerequisites (Pro plan). It is sufficiently complete for an agent to understand the tool's purpose and invocation context.

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 schema fully documents each parameter. The description mentions 'trade, region, demand intelligence' which maps to parameters, but adds no syntax or format details beyond the schema. Baseline score 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 clearly states it is an 'AI-powered lead generation strategy engine' that analyzes trade, region, demand intelligence, and historical lead data to recommend marketing channels with costs and ROI. It distinguishes itself from siblings like 'marketing.generate_content' by focusing on strategy and channel recommendations.

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 implies usage for generating a marketing strategy and mentions the Pro plan requirement, but it does not explicitly compare with sibling tools or state when not to use it. Usage context is inferred rather than explicit.

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