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

Beyond the readOnlyHint and destructiveHint annotations, the description discloses that every reading has a sample count, and changes should be treated as indicative unless the sampling block says otherwise. It also clarifies the read is free and specifies the operational order, adding valuable behavioral context.

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 slightly lengthy due to an exhaustive list of metrics, but each sentence serves a purpose: the first defines the resource, the second adds a sampling caveat, and the third gives usage order and parameter guidance. The main purpose is front-loaded, making it efficient despite 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?

Given no output schema, the description lists all return aspects (share of voice, cited sources, competitor gaps, trend, crawlability, etc.) and includes important sampling caveats. It lacks structural details like response format or pagination, but the tool is a read-only getter with a single optional parameter, so the description is largely complete.

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?

The input schema already covers 100% of the parameter descriptions, and the description merely repeats the same drill-down behavior without adding new semantic detail. Baseline of 3 is appropriate because the schema does the heavy lifting; no additional meaning is provided beyond what's structured.

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 explicitly states the tool returns measured AI visibility (GEO) results across specific engines (ChatGPT, Gemini, Perplexity, Google AI Overviews) and lists concrete data points (share of voice, cited sources, gaps, trend, crawlability). This clearly distinguishes it from siblings like get_seo or get_scoreboard by focusing on AI/GEO 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 description provides explicit sequencing guidance: 'Read after refresh_scan and before any AI visibility or GEO work.' It also advises interpreting changes with caution due to sampling, and mentions the optional prompt_id for drill-down. This establishes clear when-to-use context without relying on sibling names.

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