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

Annotations already establish readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is known without the description. The description adds useful expectation-setting by listing exactly which monitoring facets are returned, though it says nothing about empty-state behavior or how Credit usage is scoped.

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?

Two sentences, no waste: the first front-loads what is returned, the second routes to the sibling. Every clause earns its place.

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 enumeration of returned facets does real work in telling an agent what to expect from a read tool. It is nearly complete for a three-required-parameter read call, though it stays a flat list rather than indicating structure or size of the response.

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% and all three required parameters (advertiserProjectId, context, llm_model) carry their own detailed descriptions, including how to source the project id. The description text adds no parameter-level meaning beyond the schema, so the baseline 3 applies.

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 gives a specific verb ('Get') plus resource ('AI Visibility monitoring for a project') and enumerates the payload: topics, models, cadence, saved prompts, latest run status, credit usage. It also explicitly separates itself from the sibling get-llm-prompt, so an agent can choose without opening schemas.

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?

It names the alternative (get-llm-prompt) and the condition that selects it ('for one prompt's history'), which is clear routing guidance. It does not address when to prefer list-llm-monitorings or what to do when no monitoring exists, so it falls short of full when/when-not coverage.

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