list_models

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

No annotations are provided, so the description carries the full burden. It discloses the behavioral trait of listing models with pricing, which is useful context. However, it lacks details on potential rate limits, authentication needs, response format, or pagination behavior, leaving gaps in behavioral understanding for a tool that likely involves network calls.

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, efficient sentence that front-loads the core action ('List all available AI models') and adds essential context ('on SolanaProx with their pricing'). There is zero waste, and every word contributes to understanding the tool's function without 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 the tool's low complexity (0 parameters, no output schema) and lack of annotations, the description is minimally complete by stating what it does. However, it could be more complete by addressing behavioral aspects like response format or usage constraints, which are important for a listing tool in an AI service context.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's purpose. A baseline of 4 is applied as it effectively handles the lack of parameters without unnecessary elaboration.

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 specific action ('List all available AI models') and the resource ('on SolanaProx'), with explicit mention of what information is included ('with their pricing'). It distinguishes itself from siblings like 'ask_ai', 'check_balance', and 'estimate_cost' by focusing on model enumeration rather than interaction, balance checking, or cost estimation.

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 implies usage context by specifying 'available AI models on SolanaProx', suggesting this tool is for discovering models before using them (e.g., with 'ask_ai'). However, it does not explicitly state when to use it versus alternatives like 'estimate_cost' for pricing details or provide any exclusions or prerequisites for usage.

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