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

With no annotations, the description carries the full burden and does disclose meaningful traits: it is free ("Free"), it is advisory only (no legal/tax advice), it returns sourced cost data, and it flags "when not to buy" the paid plan. It does not discuss caching, rate limits, or freshness of the verdict, which is the main remaining gap for a zero-annotation tool.

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 usage instruction is front-loaded and the whole definition fits in three sentences. It is dense with parentheticals and inline field names ("risk_without_plan", "why_pay") that add slight clutter, but each clause conveys real information about outputs rather than padding.

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?

There is no output schema, so the description must explain returns and it does: the four verdict values with a traffic-light framing, ban reasons, sourced mistake costs, plan value, and the price-vs-cost verdict. Covers what an agent needs to decide and call the tool, though it omits how trustable/sourced the verdict input data is.

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 coverage is 100%, so country, platform, automated and ai_content (with defaults) are already documented. The description only re-frames them collectively ("agent-run setup with AI content ... from a given country") without adding format, allowed values, or interaction details beyond the schema. Baseline 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 states a specific action ("Returns a verdict ... whether an agent-run setup with AI content can earn there from a given country") and enumerates the payload: verdict, ban reasons, documented mistake costs, plan value, price-vs-cost calculation. This is clearly an eligibility/verdict check, distinguishable from list_platforms or get_platform. It never names a sibling tool explicitly, so it stops short of full differentiation.

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?

"Use before automating anything on a platform" gives an explicit trigger condition, and the closing sentence sets boundaries ("No legal or tax advice; never use it to bypass KYC or platform rules"). However, it never points to an alternative tool or says when another sibling such as recommend would be the better call.

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