model_info

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: viewing available models, their specializations, and the ability to override auto-selection. It explains the default behavior (auto-selection based on query intent) and the override capability, though it doesn't specify what happens when no parameter is provided (e.g., whether it returns a list or default info).

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 appropriately sized and front-loaded with the core purpose in the first clause. Both sentences earn their place: the first establishes what the tool does, and the second explains the default behavior and context. There's no wasted language or 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 moderate complexity (1 optional parameter with full schema coverage, no output schema), the description is mostly complete. It covers purpose, usage, and parameter context well. However, it doesn't specify what the tool returns (e.g., a list of models with details or just confirmation), which would be helpful since there's no output schema.

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 baseline is 3. The description adds value by explaining the context of the parameter: 'manually override model selection' and 'By default, models are auto-selected based on query intent'. This provides semantic meaning beyond the schema's enum descriptions, helping the agent understand when and why to use the parameter.

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's purpose with specific verbs ('View available Perplexity models and their specializations, or manually override model selection') and distinguishes it from sibling tools like 'search' or 'list_filters' by focusing on model information and selection rather than filtering or searching operations.

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 explicitly provides usage guidance: 'By default, models are auto-selected based on query intent (research, reasoning, general search)' and indicates when to use the override parameter. This clearly distinguishes it from the default auto-selection behavior and helps the agent understand when manual selection is appropriate.

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