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

With no annotations, the description carries the disclosure burden and does well: it explains excluded onboarding platforms, dataStatus=no_data semantics, null rules for citationPresence/shareOfVoice, and the 0–1 vs 0–100 ranges. It does not state read-only/auth behavior, but for a summary read tool it supplies substantial context about what is excluded and how to interpret edge cases.

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 purpose is front-loaded, but the remainder is a dense, run-on set of caveats that would be easier to scan as a short list. Many sentences earn their place, but the lack of structure and high density hurt readability.

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?

Because there is no output schema and no annotations, the description must carry return-value semantics, and it largely does: it covers platform reporting scope, data-status meanings, organic percentage ranges, citation-presence nulls, and advertising metric separation. Some details remain implicit, but it is substantially complete for a complex read tool.

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 four parameters are already documented. The description adds little parameter-specific meaning beyond schema; it explains output interpretation more than the filters themselves, so the 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 begins with a specific verb and resource: 'Get AI search visibility summary metrics for a project.' It implies a summary-level view rather than per-response detail, but does not explicitly name sibling tools like devtune_get_visibility_response or devtune_get_search_rollups, so sibling differentiation is left to inference.

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?

Usage is implied by 'summary metrics' and the default reporting behavior, and the description gives parameter guidance such as using includeHistoricalPlatforms only when explicitly requested. However, it never states when to choose this tool over sibling visibility or traffic tools, leaving the agent to infer the summary-vs-detail distinction.

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