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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the safety bar is covered. The description adds genuinely useful context beyond that: per-call pricing of ¥0.3, billing only on success, and automatic refund on failures or empty results, which signals to the agent that empty responses are possible and non-billable. It omits pagination/result-count limits, which is the main remaining 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?

Three tight sentences, front-loaded with purpose then use cases then cost, with no filler. It is dense but every clause carries information an agent needs (scope, return contents, chaining, billing).

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, and the description compensates by describing the return payload (post list, content IDs, basic engagement metrics) and the follow-up calls those IDs enable. It does not state how many results are returned or whether paging exists, which is a notable omission for a search tool.

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 100%, so the baseline is 3, but the description adds real meaning to the required `query` argument by clarifying it matches across users, hashtags, and locations rather than just post text. The `_token` fallback auth param is covered by the schema itself, so no compensation was needed there.

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+resource (Instagram comprehensive search) and explicitly scopes the entity types searched (users/hashtags/locations), plus what comes back (post list with content IDs and engagement data). This clearly separates it from siblings like ig_user_posts or ig_post_detail, which are entity- or ID-scoped rather than keyword-scoped.

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?

Gives concrete use cases ('keyword monitoring, content discovery, or building candidate sets') and a downstream hint that returned IDs feed the detail and comment endpoints, effectively routing the agent into a workflow. It stops short of naming an explicit alternative or stating when NOT to use it (e.g. searching within one account, which ig_user_posts covers).

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