edit_audience

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

No annotations are present, so the description fully carries the burden. It discloses async behavior, return payload shape, polling requirements, success/failure states, concurrency limits, and the 'already_running' notice. This goes beyond a simple statement of side effects and gives the agent a complete mental model of the tool's runtime behavior.

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?

The description is tightly written in four sentences, each earning its place. It front-loads the core purpose, then covers required parameters, async workflow, and constraints without redundancy or fluff.

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?

Given the tool's complexity (async, stateful, concurrent limits) and lack of output schema, the description explains everything an agent needs: how to specify changes, what response to expect, how to poll, and typical timing. It also references sibling tools appropriately, making the description self-sufficient 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 coverage is 100% and parameter descriptions are already clear. The description adds value by emphasizing that `sections` is REQUIRED, giving concrete examples, and directing users to preview_audience_edit to discover valid keys. This is above the baseline, though much of the parameter meaning is already present in the schema.

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 clearly states the tool performs an async LLM AI edit of the brand's audience/ICP, using a specific verb ('ai-edit') and resource. It distinguishes itself from sibling tools like get_audience (read) and preview_audience_edit (preview) by emphasizing that it edits and returns an operationId for async polling.

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?

Provides explicit usage context: describes the intended use for editing audience sections, instructs users to call preview_audience_edit first if unsure about valid section keys, and warns about the single-running-edit constraint. It also specifies the async polling workflow and typical duration, offering clear when-to-use and when-to-check alternatives.

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