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get_model_info

Retrieve detailed specifications and configuration data for a specific vLLM model to understand its capabilities and requirements.

Instructions

Get detailed information about a specific model

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesThe ID of the model to get info for

Implementation Reference

  • Main handler function get_model_info that validates model_id argument, calls VLLMClient to fetch model information, and formats the response as TextContent with JSON-formatted model details.
    async def get_model_info(arguments: dict[str, Any]) -> list[TextContent]:
        """
        Get detailed information about a specific model.
    
        Args:
            arguments: Dictionary containing:
                - model_id: The ID of the model to get info for
    
        Returns:
            List of TextContent with detailed model information.
        """
        model_id = arguments.get("model_id")
        if not model_id:
            return [TextContent(type="text", text="Error: No model_id provided")]
    
        try:
            async with VLLMClient() as client:
                model_info = await client.get_model_info(model_id)
    
                if not model_info:
                    return [
                        TextContent(type="text", text=f"Model '{model_id}' not found on the server.")
                    ]
    
                # Format model info
                result = f"## Model: {model_id}\n\n"
                result += "```json\n"
                result += json.dumps(model_info, indent=2)
                result += "\n```"
    
                return [TextContent(type="text", text=result)]
    
        except VLLMClientError as e:
            return [TextContent(type="text", text=f"Error getting model info: {str(e)}")]
  • VLLMClient.get_model_info helper method that retrieves model information by listing all models and finding the one matching the given model_id.
    async def get_model_info(self, model_id: str) -> Optional[dict[str, Any]]:
        """Get information about a specific model."""
        models = await self.list_models()
        for model in models:
            if model.get("id") == model_id:
                return model
        return None
  • Tool registration defining get_model_info with its name, description, and inputSchema that requires a model_id string parameter.
    Tool(
        name="get_model_info",
        description="Get detailed information about a specific model",
        inputSchema={
            "type": "object",
            "properties": {
                "model_id": {
                    "type": "string",
                    "description": "The ID of the model to get info for",
                },
            },
            "required": ["model_id"],
        },
  • Handler mapping in call_tool function that routes 'get_model_info' tool calls to the get_model_info handler function.
    elif name == "get_model_info":
        return await get_model_info(arguments)
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states this is a read operation ('Get'), implying it's likely safe and non-destructive, but doesn't disclose any behavioral traits such as authentication needs, rate limits, error conditions, or what 'detailed information' includes (e.g., metadata, performance metrics).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'detailed information' entails in the return value, nor does it address potential complexities like error handling or usage constraints, leaving gaps for an AI agent to infer behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with the single parameter 'model_id' clearly documented in the schema. The description adds no additional meaning beyond implying it's for a 'specific model', which the schema already covers. This meets the baseline of 3 when schema coverage is high.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'detailed information about a specific model', making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_models' or 'vllm_status', which might provide overlapping or related information about models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. With siblings like 'list_models' (likely listing multiple models) and 'vllm_status' (possibly checking model status), there's no indication of when this specific 'get' operation is preferred or what prerequisites might exist.

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

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