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

Goes well beyond the annotations: discloses the upstream data source, that bio text may be truncated by Instagram, that returned counts can be abbreviated with a `counts_approximate` flag, and enumerates fields deliberately absent (bio links, verified/private flags, avatar). It also names two concrete error codes and their causes (nonexistent account vs. Instagram login wall / rate limiting) and flags per-call cost.

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?

Front-loaded with a '[read]' marker and the core purpose, then input format, then returned fields, then cost and errors — a sensible order with essentially no filler. The return-field and error sentences run together in dense prose rather than being listed, which costs a little scannability.

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?

There is no output schema, so the description's enumeration of returned fields (`full_name`, `biography`, `follower_count`, `following_count`, `post_count`, `counts_approximate`, `url`) is exactly what an agent needs. Combined with the single-parameter input, cost model, and failure modes, nothing required to call it correctly 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?

Schema coverage is 0% and the parameter schema is a bare string, so the description carries the full burden — and it does specify that the leading '@' is optional and that a full instagram.com/name address is also accepted. Only one parameter exists and its accepted shapes are covered; no further semantics (length, case sensitivity) are needed.

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?

States a specific verb and resource ('One Instagram account's light profile') and immediately bounds the scope with '(no posts)' and the data source ('meta description of its public page through the Instagram Trends gateway'). That is enough to separate it from siblings like get_tt_user, get_yt_channel, and search_ig_trends without opening any schema.

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?

Tells the agent what input to supply, that the call is a priced read, and what error conditions map to which outcome, which sets clear expectations for when a call is worthwhile. It does not explicitly name alternatives or say when-not to use it (e.g. 'for posts use list_social_posts'), so it stops short of full routing guidance.

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