openai_invite_user

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

With no annotations provided, the description carries the full behavioral burden. It discloses key non-obvious behaviors: the invite must be accepted before access, status starts at pending, and the projects parameter has legacy behavior (default project) versus empty list (no projects). It does not mention error conditions or idempotency, but the disclosed behaviors are significant and useful.

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 well-structured: a clear one-sentence purpose, a brief clarifying note about the invite lifecycle, and a cleanly formatted Args section. Every sentence carries necessary information without fluff, and the front-loaded purpose makes the tool's intent immediately clear.

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 (3 parameters, nuanced projects behavior) and the presence of an output schema, the description is complete. It covers the full invite lifecycle, parameter semantics, and edge cases. No major gaps remain; the output schema handles return-value details.

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 0%, so the description must compensate. It thoroughly explains all three parameters: email (address), role (allowed values owner/reader), and projects (format, optional, special meanings of omission vs empty list). This is far more informative than the bare schema, which lacks descriptions.

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 starts with a specific verb+resource: 'Invite a new user to the OpenAI organization by email.' This clearly distinguishes it from sibling tools like add_project_user (adds existing user) and list_invites (lists invites), making the tool's unique purpose obvious.

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?

The description provides context on when to use this tool by explaining the invitation flow ('must be accepted', 'status starts at pending') and clarifies behavior of the projects parameter (omitted vs empty list). However, it does not explicitly name alternatives or say 'use this instead of X', which would differentiate it from tools like add_project_user for existing users. Still, the usage context is reasonably clear.

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