List LLM Models

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

With no annotations provided, the description carries full burden but lacks important behavioral details. It doesn't mention whether this is a read-only operation, what authentication might be required, rate limits, pagination behavior (beyond the 'limit' parameter), or what the response format looks like. The description only covers basic functionality without behavioral transparency.

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 perfectly concise - a single sentence that efficiently communicates the core purpose, key capabilities, and available filters/sorting options. Every word earns its place with zero wasted text.

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 annotations and no output schema, the description is incomplete for a tool with 4 parameters. It doesn't explain what the return values look like (structure, fields, data types), error conditions, or important behavioral aspects like whether this makes external API calls. The description covers basic functionality but leaves critical contextual gaps.

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 description coverage is 100%, so the schema already fully documents all parameters. The description mentions filtering by creator and sorting options, which aligns with the schema but doesn't add meaningful semantic context beyond what's already in the parameter descriptions. This meets the baseline for high schema coverage.

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 verb ('List') and resource ('all available LLM models from Artificial Analysis') with specific attributes included (pricing, speed, benchmark data). It distinguishes from the sibling 'get_model' by emphasizing comprehensive listing rather than retrieving a specific model.

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 context for when to use this tool (to get a filtered/sorted list of models with pricing and benchmark data). It doesn't explicitly state when NOT to use it or name alternatives, but the sibling tool 'get_model' is implied as an alternative for single-model retrieval.

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