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

With no annotations provided, the description carries the full burden and delivers richly: it discloses credit cost ('~45 credits (0.9/result)'), billing safety ('Empty results and failures are never charged'), caching behavior ('cache=true for a free 24h cache hit'), and a data-availability caveat ('when Instagram exposes them'). These are genuine behavioral traits that annotations would otherwise need to cover.

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?

Four short sentences, each earning its place: purpose/content first, then cost, billing guarantee, and caching. The most decision-relevant information (what it returns and the caveat) is front-loaded. There is zero fluff or repetition of schema content.

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 tool with no annotations and no output schema, the description plus schema cover the essentials well: return content, platform scope, cost, billing, caching, pagination mechanics, and URL validation. The main gap is that pagination flow (hasMore/nextCursor) and potential response shape are only implied via the schema's cursor parameter rather than described, so an agent may not know when to stop paging.

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 schema already documents url, cache, limit, and cursor thoroughly, including the default/max for limit and the nextCursor format. The description adds some value by tying cost to results ('0.9/result'), which clarifies limit's billing implication, and by restating the cache default ('default always fresh'). This is baseline-3 territory: schema does the heavy lifting, description adds marginal color.

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 opens with a specific verb and resource: 'Get the comments on any Instagram post or Reel — text, author, likes, and timestamp.' This clearly defines scope (any post or reel), the platform (Instagram), and the returned fields. Among the many sibling comment tools (facebook_comments, tiktok_comments, youtube_comments), the platform-specific naming and content make it unambiguous.

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 context is implied rather than explicit: the description says it gets Instagram post/reel comments, so an agent can infer when to call it. However, it never names alternatives or states when-not-to-use it (e.g., no routing to facebook_comments or youtube_comments for other platforms). The schema does add a platform-match exclusion, but the description itself offers no explicit usage guidance.

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