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

Annotations already mark it as read-only and non-destructive, and the description adds valuable behavior: it uses the user's OpenRouter key, makes external LLM calls, and costs $0.10/call with x402 payment details. This goes beyond annotations by disclosing cost, external integration, and the operational model without contradiction.

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 delivers the core purpose in the first sentence, with the second sentence adding a concise definition of GEO citation tracking. The pricing and payment info is brief. The marketing metaphor 'backlink profile of the AI-answer era' is slightly non-essential but not wasteful. It is efficiently structured and front-loaded.

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 no output schema, the description could have clarified return format, but it does state that users 'see which sources and domains' are cited, which conveys the core output. Complexities like multiple models and billing are left to the schema. The description is adequate for a straightforward read tool but not exhaustive.

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 baseline is 3. The description adds marginal value by mentioning 'your own OpenRouter key' and pricing, but most parameter semantics (like row multiplication, concurrency limits) are already in the schema. No new parameter-level insights are provided.

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 uses a specific verb ('see') and resource ('sources and domains AI assistants cite'), making the tool's purpose immediately clear. It distinguishes itself from siblings by focusing on AI citation tracking (GEO) rather than change alerts, crawler access, or brand visibility.

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 phrase 'For each query that matters' implies the tool is used for tracking citations across AI assistants, and 'GEO citation tracking' suggests a use case. However, it does not explicitly compare itself to sibling tools like llm-brand-visibility or provide when-not-to-use guidance. Usage is implied rather than explicitly delineated.

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