get_instance_types
Retrieve available instance types for cloud providers to optimize resource selection for ML model deployment. Supports AWS, GCP, Azure, and more.
Instructions
Get available instances types for a cloud provider
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| cloud_provider | Yes | The cloud provider to get instance types for |
Implementation Reference
- src/brev_mcp/tools.py:10-21 (handler)MCP tool handler function that validates the cloud_provider argument, fetches instance types using get_provider_instance_types, and formats the response as TextContent.async def get_instance_types_tool(args: dict[str, str]) -> TextContent: if "cloud_provider" not in args: raise ValueError("cloud_provider argument is required for get_instance_types tool") cloud_provider = CloudProvider(args["cloud_provider"]) instance_types = await get_provider_instance_types(cloud_provider) return [ TextContent( type="text", text=instance_types ) ]
- src/brev_mcp/tools.py:42-51 (schema)Input schema definition for the get_instance_types tool, specifying the required cloud_provider parameter with enum values from CloudProvider.inputSchema={ "type": "object", "properties": { "cloud_provider": { "description": "The cloud provider to get instance types for", "enum": [provider.value for provider in CloudProvider] } }, "required": ["cloud_provider"] }
- src/brev_mcp/tools.py:38-54 (registration)Registration of the get_instance_types tool in the tool_models dictionary, linking the tool schema, description, and handler function."get_instance_types": ToolModel( tool=Tool( name="get_instance_types", description="Get available instances types for a cloud provider", inputSchema={ "type": "object", "properties": { "cloud_provider": { "description": "The cloud provider to get instance types for", "enum": [provider.value for provider in CloudProvider] } }, "required": ["cloud_provider"] } ), call_tool=get_instance_types_tool ),
- src/brev_mcp/instance_types.py:16-32 (helper)Helper function that retrieves all instance types from the API, filters and selects those for the given provider, and returns a formatted JSON string.async def get_provider_instance_types(provider: CloudProvider)-> str: try: all_instance_types_obj = await get_instance_types() instance_types = filter_instance_types(all_instance_types_obj) for cloud_provider, instance_type_list in instance_types.items(): logger.info(f"Number of instance types for {cloud_provider.value}: {len(instance_type_list)}") if provider not in instance_types: raise ValueError(f"Provider {provider.value} not found in instance types") instance_type_dicts = [ instance_type.model_dump(exclude_none=True) for instance_type in instance_types[provider] ] return json.dumps(instance_type_dicts, indent=2) except Exception as e: logger.error(f"Error getting instance types: {str(e)}") raise RuntimeError(f"Failed to get instance types: {str(e)}")
- src/brev_mcp/api.py:13-32 (helper)Core helper function that performs the HTTP API call to Brev's endpoint to fetch all available instance types, validates the response, and returns the parsed object.async def get_instance_types() -> AllInstanceTypeObj: access_token = get_acess_token() org_id = get_active_org_id() try: async with httpx.AsyncClient(timeout=httpx.Timeout(25.0)) as client: response = await client.get( f"{BASE_API_URL}/instances/alltypesavailable/{org_id}", headers={ "Authorization": f"Bearer {access_token}", "Content-Type": "application/json" }, ) response.raise_for_status() data = response.json() all_instance_types_obj = AllInstanceTypeObj.model_validate(data) return all_instance_types_obj except ValidationError as e: raise RuntimeError(f"Failed to validate instance types: {str(e)}") except Exception as e: raise RuntimeError(f"Failed to get instance types: {str(e)}")