Select the optimal AI model (GPT-4, Claude, Gemini) for a task based on type, cost, and provider preferences.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | No | Task description for auto-classification | |
| task_type | No | ||
| prefer_tier | No | ||
| input_tokens | No | ||
| max_cost_usd | No | Max budget in USD per call | |
| output_tokens | No | ||
| prefer_provider | No |
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.
Add one secure layer between your agents and this server.
list_models is distinctly informational, while compare_models and route_model both relate to model selection. However, compare_models provides a comparison output, whereas route_model gives a recommendation, so they serve separate intents with clear descriptions.
All tool names follow the verb_noun pattern with lowercase and underscores: compare_models, list_models, route_model. Consistent and predictable.
Three tools is well-scoped for a model router: listing, comparing, and routing. Each tool earns its place without redundancy.
The tool surface covers the full workflow: discover available models, compare them, and select one. No obvious gaps for the stated purpose.