ai-model-router

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

No annotations are provided, so the description must convey behavioral traits. It only states the core function without detailing return format, side effects, or input requirements. It does not disclose whether it's read-only or if parameters have constraints.

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?

One sentence of 15 words, no redundancy, and the main action is front-loaded. Every word earns its place.

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?

With no annotations, no output schema, and a sparse description, the tool's behavior is underspecified. The description does not explain how parameters interact, what constitutes a valid task_type, or what the comparison output looks like.

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% coverage, and the description provides only a hint that the tool works 'for a given prompt or token count', which loosely maps to prompt/input_tokens/output_tokens but does not clarify task_type or parameter relationships. This is insufficient compensation for the complete lack of 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 uses the specific verb 'compare' and identifies the resource as 'all supported AI models', clearly distinguishing from sibling tools like list_models and route_model. The scope ('cost and task fit') is explicit.

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 for comparing cost and task fit, but does not explicitly mention when to use it over list_models or route_model, nor does it state exclusions or prerequisites. It offers clear context but lacks explicit alternative guidance.

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, the description must disclose behavioral traits itself. It only states that the tool lists models with pricing and capabilities, without mentioning read-only behavior, rate limits, pagination, or side effects. This is a minimal disclosure for a list operation.

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 that directly conveys the core function. It is front-loaded and contains no unnecessary words, achieving good conciseness.

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?

For a tool with no parameters and no output schema, the description provides enough context about what the tool returns (a list of models with pricing and capabilities). It could be more explicit about being a read-only overview, but the simplicity of the tool makes this adequate.

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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameters because there are none. It adds no parameter-specific meaning but also does not need to.

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 lists all supported AI models with pricing and capabilities. The verb 'list' and resource 'supported AI models' establish a specific purpose that distinguishes it from siblings like compare_models and route_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?

Usage guidance is implied but not explicit. The description indicates the tool is for listing all models, but it does not mention when to use this versus comparing or routing models. No alternatives or exclusion criteria are provided.

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 carries the full burden for behavioral transparency. It does not disclose whether the tool is read-only, how it handles the prompt or cost constraints, or whether it invokes the model or merely recommends one. This lack of detail is a significant gap for a tool with zero annotation coverage.

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, focused sentence with no filler. It front-loads the action ('Select the optimal AI model') and earns its place.

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?

With 7 optional parameters, no output schema, and sparse annotation support, the description remains quite brief. It does not state what the tool returns, how auto-classification works, or the interaction between cost and tier preferences. Given the moderate complexity and low schema coverage, more detail is needed for full contextual completeness.

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 only 29% (2/7 parameters have descriptions). The tool description mentions 'type, cost, and provider preferences', which maps to task_type, max_cost_usd, and prefer_provider, but it does not explain the role of prompt, input_tokens, output_tokens, or prefer_tier. This leaves several parameters under-specified for the agent.

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 function: 'Select the optimal AI model (GPT-4, Claude, Gemini) for a task based on type, cost, and provider preferences.' This specific verb+resource distinguishes it from siblings 'compare_models' and 'list_models' which imply comparison and listing rather than selection.

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 when to use it (when you need to select a model based on task criteria), but it does not explicitly mention alternatives or exclusions like 'use compare_models for side-by-side comparison'. This leaves usage relative to siblings somewhat implicit.

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