anythingllm_list_models

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

Annotations already flag the operation as read-only, idempotent, and non-destructive. The description adds the meaningful context that listing is performed via an OpenAI-compatible endpoint, which clarifies API access semantics beyond what annotations convey.

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 a single, front-loaded sentence with no redundancy. Every word adds value, clearly stating the operation and the endpoint type.

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?

For a zero-parameter, read-only list operation with an output schema, the description fully captures the purpose and endpoint context. There is nothing missing for an agent to invoke the tool correctly.

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 tool has zero parameters, so the description does not need to explain parameter details. The baseline of 4 applies because there are no parameter semantics to elaborate on.

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 'List available models via the OpenAI-compatible endpoint' clearly states the action (list) and the resource (models). It distinguishes itself from sibling tools like list_workspaces and list_documents by specifying the domain of models.

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 tool's name and description make it obvious that it is used to enumerate models. While it doesn't explicitly state when not to use it, no alternative sibling tool serves the same purpose, so the intent is clear.

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