get_available_models

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

Describes the tool as a read operation (list), explains output structure (model IDs, status, pricing), and notes live pricing behavior, with no annotation contradictions.

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?

Four concise sentences, each adding value: purpose, compatibility, usage, output and next steps. No redundancy.

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 no parameters and existing output schema, the description fully covers usage context, output details, and related tool, making it complete.

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?

Zero parameters; schema coverage 100%. Baseline 4. Description adds no parameter info but correctly explains output, which is irrelevant for parameter semantics.

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 it lists all AI models with live pricing and highlights drop-in compatibility, distinguishing it from other tools.

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 tells when to use: when user asks which model or wants alternatives to GPT-4o/Claude. Also suggests next step (calculate_savings).

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