find_citation_leaders

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

While the annotations already declare readOnlyHint=true and idempotentHint=true, the description builds on this with 'Read-only: no side effects, safe to retry' and adds the quota cost ('Costs 10 quota units/call (free tier: 3 checks/month total across all tools)'). It also details the exact return structure, including conditional fields, which goes well beyond the annotations without contradicting them.

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 organized into scannable sections (purpose, usage guidance, args, returns) and front-loads the core value proposition. It is slightly verbose with examples and caveats, but every sentence contributes to correct usage, so it earns a 4 rather than a 5. A tighter edit could trim redundant phrasing like 'Read-only: no side effects' (already implied by the annotation) to improve density.

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?

Despite having no output schema, the description fully documents the return JSON, including nested objects and the conditional 'compare_domain_present' field. Combined with explicit parameter details, platform constraints, quota information, and sibling references, an agent has everything needed to call this tool correctly without external lookup. The description fully compensates for the lack of structured 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?

Even though the context signal reports 0% schema coverage, the description compensates with an 'Args:' section that explains every parameter in plain language, provides an example value for 'keyword', lists valid options for 'platform' (including a default), and clarifies the 'compare_domain' format. This adds far more meaning than the sparse schema, making the tool callable with confidence.

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 opens with a specific verb and resource ('Find which domains dominate AI-answer citations for a topic/keyword') and immediately distinguishes itself from siblings by framing the use case as 'who's winning AI search for this topic' or 'is my competitor cited more than me for X.' It also names a sibling (check_ai_visibility) as an alternative, making the tool's unique purpose unmistakable.

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?

Provides explicit when-not-to-use guidance: 'Use check_ai_visibility instead if you already know which domain you care about and just want its own citation count, not a leaderboard.' It also discloses platform limitations ('Perplexity and Gemini aren't available') and gives example queries, so an agent knows exactly when to select this tool over alternatives.

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