estimate_audience_size

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

With no annotations provided, the description carries the burden. It discloses behavioral traits such as optional access_token with cached token fallback, account_id being required for comprehensive estimation, and deprecated parameters. It does not explicitly state side effects or rate limits, but the 'estimate' action implies read-only behavior. The disclosure is reasonably thorough.

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 organized with an intro, Args block, and Returns section. It is somewhat long but each sentence serves a purpose. The example for targeting is illustrative, and deprecated notes are clearly marked. A minor redundancy is the 'This function provides...' sentence which could be implicit, but overall structure is efficient.

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 six-parameter complexity, zero annotations, and no schema descriptions, the description covers every parameter, provides an example, notes defaults, explains the return value, and flags deprecated fields. It even mentions the API name. This is sufficient for an agent to select and invoke the tool effectively.

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%, so the description must explain all parameters. It does so thoroughly: access_token (cached fallback), account_id (format and requirement), targeting (with a full example), optimization_goal (default and options), and interest_list/interest_fbid_list (deprecated). This adds substantial meaning beyond the raw 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 opens with a specific verb+resource: 'Estimate audience size for targeting specifications using Meta's delivery_estimate API.' This clearly states what the tool does and distinguishes it from sibling tools like search_interests or search_behaviors, which are search utilities rather than estimation endpoints.

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 clear context: the tool is for comprehensive audience estimation for complex targeting combinations, and also maintains backwards compatibility for simple interest validation. This implies when it should be used (complex targeting) and notes the deprecated fallback usage. However, it does not explicitly name alternatives or state when not to use it.

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