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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful behavioral context: the data source, default dimensions, combinable dimensions, default date range, and the exact site_url format expectation. It stops short of describing response shape or pagination, which is a minor gap for a read-only analytics 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?

Front-loaded with the main action and metrics, then organized into modes and routing guidance. Every sentence earns its place, with no filler or duplication of the schema.

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?

For a read-only analytics tool, the description covers the data source, metrics, dimensions, defaults, filters, and routing prerequisite. The detailed schema covers all parameters and the annotations cover safety, leaving no critical gap for an agent to invoke it correctly.

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 restates key parameter usage such as dimensions values, defaults, and optional filters, but does not add meaning materially beyond what the schema already provides.

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?

States the exact resource ('search performance data from Google Search Console'), the metrics returned (clicks, impressions, CTR, average position), and the two dimension modes (query/page). It also differentiates itself from sibling data tools by framing this as the SEO/organic-search entry point.

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 use cases ('SEO performance, keyword rankings, organic traffic, search visibility, per-page SEO') and concrete routing guidance: call get_site_list first if site_url is unknown, use dimensions=['page'] for page SEO, and notes there is no second page door. This leaves no ambiguity about when to select this tool.

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