lbm_list_models

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

Annotations already indicate read-only and idempotent. Description adds return format details (compact CSV by default, full JSON with pricing) and parameter effect, extending beyond annotations without contradiction.

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?

Two concise sections (WHEN TO USE, RETURNS) plus a parameter note. No redundant sentences, front-loaded with key usage guidance.

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?

With no output schema, description adequately explains return format and parameter behavior. Provides sufficient context for correct invocation given tool simplicity.

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%. Description adds value by explaining the effect of include_all_fields and recommending default (compact CSV). Also describes what is returned, supplementing 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 discovers available LLM models for brand monitoring scans, with explicit direction to call it before creating a project or running a scan. It distinguishes itself from sibling tools focused on scans and projects.

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?

Explicitly says when to use (before creating project or running scan) and guides on include_all_fields parameter. Lacks mention of when not to use or explicit alternatives, but context is sufficient given sibling diversity.

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