ai-model-router

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

With no annotations, the description shoulders the full burden. It states what the tool does but not how it behaves (e.g., read-only, synchronous, rate limits, or what the output looks like). This is minimal disclosure.

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?

A single, front-loaded sentence with no filler. Every word earns its place, efficiently conveying the core purpose.

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 4 parameters, no annotations, and no output schema, the description lacks detail on return format, required parameters (none required), and how to combine parameters. It does not fully equip the agent to use the tool correctly.

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 0%, so the description must compensate. It mentions 'prompt or token count' but does not explain the 'task_type' parameter or clarify relationships between parameters (e.g., mutual exclusivity). This is insufficient for 4 parameters.

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 compares cost and task fit across all supported AI models, using either a prompt or token count. This distinguishes it from sibling tools like 'list_models' (which likely just lists) and 'route_model' (which likely selects a 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 implies usage when comparing models based on prompt or token count, but provides no explicit guidance on when not to use it or when to prefer siblings. No alternatives are named, so the agent must infer context.

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

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

With no annotations provided, the description carries full burden. It only states the basic operation (listing models) without disclosing any behavioral traits such as read-only nature, rate limits, authentication requirements, or pagination behavior.

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, clear sentence with no unnecessary words. It efficiently conveys the tool's purpose and output content.

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?

The description adequately explains that the tool lists all models with pricing and capabilities, but it could be more specific about the output format or what 'capabilities' entails. Lacking an output schema, the description is the sole source of information on return values.

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?

There are no parameters (0 params), so baseline is 4. The description adds value by indicating output includes 'pricing and capabilities', which supplements the empty 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 verb 'List' and the resource 'all supported AI models', and specifies the included information (pricing and capabilities). It implicitly distinguishes from sibling tools 'compare_models' and 'route_model' by focusing on listing rather than comparing or routing, but does not explicitly differentiate.

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 no guidance on when to use this tool versus its siblings (compare_models, route_model). It does not mention any prerequisites or context for optimal use.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. However, it only mentions the selection criteria without explaining how conflicts (e.g., cost vs. quality) are resolved, what 'optimal' means algorithmically, or any side effects like API calls. This is insufficient 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 a single sentence, concise and front-loaded. However, it could be slightly expanded to cover key parameters without becoming verbose. It earns its place but lacks structure (e.g., bullet points).

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 has 7 parameters, no required parameters, no output schema, and moderate complexity, the description does not provide enough context. It omits details about return format, behavior when parameters are missing, and fallback logic for conflicting preferences, making it incomplete for an agent to use confidently.

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 low (29%), and the description does not compensate for undocumented parameters like 'input_tokens' or 'output_tokens'. While it maps to 'task_type', 'prefer_tier', and 'prefer_provider', it adds no extra semantic detail beyond the enum names. Only 'prompt' and 'max_cost_usd' have schema descriptions.

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 action ('Select') and resource ('optimal AI model'), and specifies the basis for selection (task type, cost, provider preferences). It distinguishes from siblings 'compare_models' and 'list_models' by focusing on selection rather than comparison or listing.

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 no guidance on when to use this tool versus alternatives like 'compare_models' or 'list_models'. It lacks explicit context, exclusions, or mentions of prerequisites, leaving the agent to infer usage scenarios.

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