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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description adds genuine value beyond that by disclosing what results carry (source, freshness, restrictions, community counts) and warning that an empty result set is not proof of absence — a non-obvious behavioral trait for this domain.

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?

Purpose is front-loaded in the first sentence, followed by input guidance, parameter clarifications, result shape, and a caveat in that order. It is dense but every sentence adds a distinct piece of information; minor redundancy in the split between input and parameter sentences.

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 an output schema present, the description need not enumerate return values, yet it still summarizes result fields usefully. Five optional parameters, zero required, and strong annotation coverage mean the remaining omission (pagination semantics) is not critical for correct invocation.

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 0%, so the description must carry parameter meaning. It does explain country as an ISO two-letter code, clarifies that audiences supplement rather than replace 'anyone' offers, and describes query usage — three of five parameters. Limit and offset are undocumented but follow conventional pagination naming, leaving a modest gap.

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 states a specific verb and resource: 'Find AI/cloud credit programs and reviewed public promo codes', which tells an agent exactly what corpus is searched. The 'reviewed public' qualifier hints at curation versus sibling tools like search_credits or get_offer, but no sibling is named explicitly, so differentiation is implicit rather than stated.

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 input guidance ('Use a provider name or keywords') and a useful caveat that empty results do not prove no promotion exists, which shapes follow-up behavior. However, it never states when to pick this over search_credits, search, or get_offer, and provides no exclusions or prerequisites.

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