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

No annotations exist, so the description carries the full disclosure burden. It hints that the output is a 'prompt and operating playbook,' which is useful, but says nothing about return format, length, whether anything is persisted, or what the query actually changes. For a tool with zero annotation coverage, that is a substantial gap.

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?

Only three sentences, but the first and last are pure marketing ('Save hours every week', 'No setup, works in any LLM chatbot') and earn no space. The one informative clause about delivering a prompt and playbook is buried in the middle instead of being front-loaded.

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?

The tool is presumably simple (one optional param, no output schema), but the description never states what a caller actually receives or does with the result, nor how the query scopes it. Given no annotations and no output schema, the description needed to supply that context and does not.

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 optional parameter ('query') with 100% schema description coverage, so the schema already explains it as scoping the analysis. The description adds no meaning beyond that, but the baseline of 3 applies when the schema does the work.

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 identifies the resource ('ugc campaign builder toolkit') and a domain ('restaurant/brand growth'), but the verb is soft marketing language ('gives you a battle-tested prompt and operating playbook') rather than a concrete action. It never distinguishes itself from close siblings such as ugc-campaign-workbench, fnb-ugc-campaign, or restaurant-ugc-campaign, so an agent cannot tell which of these to pick.

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?

There is no when-to-use guidance, no prerequisites, and no mention of any alternative or exclusion. 'No setup, works in any LLM chatbot' is a selling point, not a usage condition. The agent is left to infer that this tool should be called for any UGC campaign need, which is exactly the ambiguity that causes sibling collisions.

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