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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely non-derivable context: 'Marks nothing as seen' discloses a side-effect the agent would otherwise assume, and the enumerated return contents set expectations for what comes back.

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 trigger is front-loaded in the first clause, followed by return contents and next steps. Dense but every sentence carries information; the return-value enumeration is slightly list-heavy but not padded.

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 single-parameter tool with an output schema, the description covers the trigger, the shape of the result, a notable side-effect absence, and successor tools. Nothing an agent needs to select or call it is missing.

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?

There is a single parameter with 100% schema description coverage that already explains brandSlug and the omit-to-default behavior. The description adds nothing about the parameter, so the baseline of 3 applies when the schema carries the load.

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 names a specific capability — the share of tracked AI answers mentioning the brand, its change against the prior window, new insights since the last visit, and the first mention — and anchors it to a user intent ('how the brand is doing or what is new'). It also distinguishes itself from siblings by naming parse_next_tasks and parse_evidence as follow-ons.

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 gives an explicit trigger ('Use when the user asks how the brand is doing or what is new') and routes the agent onward with 'Next: parse_next_tasks for what to do; on Pro, parse_evidence for the answers behind an insight.' When-to-use and alternatives are both stated.

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