invokeai_models

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does a strong job: install runs asynchronously; polling is done via operation='installs'; the return format is specified; installs may download multiple GB; model_type taxonomy is described. It does not go into depth on delete/update side effects, but the critical async and return behaviors are well covered.

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 with sections for rationale, return format, examples, and notes. It is slightly on the longer side but each section earns its place, providing essential operational details without fluff. The front-loaded opening sentence is strong.

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 (7 parameters, multiple operations, async install, output schema exists), the description is quite complete. It covers the return format, operation set, parameter mapping, sources, and async progress polling. It does not detail every operation's behavior, but the output schema and examples fill most gaps, making this a robust description for a multi-operation 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?

The schema already has 100% coverage for parameter descriptions, so the baseline is 3. The description adds value by mapping operations to required parameters (e.g., source required for install, key for get/update/delete) and by providing examples that show how config overrides are passed. This goes beyond simply restating schema 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 opens with a specific verb+resource: 'Manage installed models in the local InvokeAI instance.' It then enumerates the operations (list, get, install, update, delete, installs, stats) and provides a rationale for why these are grouped. This clearly distinguishes it from sibling tools like show_invokeai_models_card, which is a UI card display, and other domain-specific tools (queue, gallery, etc.).

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 clear usage context by explaining the model lifecycle domain and giving concrete examples of valid calls (list, install, delete). It also notes accepted install sources and asynchronous behavior. However, it does not explicitly state when *not* to use this tool or directly contrast with the sibling show_invokeai_models_card, so it falls short of full alternative guidance.

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