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rainhan99

Cloud Manage MCP Server

by rainhan99

list_vultr_instances

Retrieve a list of all Vultr cloud server instances to view current deployments and manage resources.

Instructions

列出所有Vultr实例

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:540-546 (handler)
    The MCP tool handler function decorated with @mcp.tool(), which registers and implements the list_vultr_instances tool by delegating to vultr_provider.list_instances().
    @mcp.tool()
    def list_vultr_instances() -> Dict:
        """
        列出所有Vultr实例
        """
        return vultr_provider.list_instances()
  • The supporting method in VultrProvider class that implements the core logic of listing all Vultr instances using the Vultr API v2 /instances endpoint, formatting the response.
    def list_instances(self) -> Dict:
        """
        列出所有Vultr实例
        
        Returns:
            Dict: 实例列表或错误信息
        """
        if not self.available:
            return {
                'error': f'Vultr服务不可用: {getattr(self, "error", "未知错误")}',
                'provider': 'vultr'
            }
        
        try:
            response = requests.get(f'{self.base_url}/instances', headers=self.headers, timeout=10)
            
            if response.status_code != 200:
                return {
                    'error': f'Vultr API调用失败: {response.status_code} - {response.text}',
                    'provider': 'vultr'
                }
            
            data = response.json()
            instances = data.get('instances', [])
            
            instance_list = []
            for instance in instances:
                instance_info = self._format_instance_summary(instance)
                instance_list.append(instance_info)
            
            return {
                'provider': 'vultr',
                'total_instances': len(instance_list),
                'instances': instance_list
            }
            
        except requests.RequestException as e:
            return {
                'error': f'网络请求失败: {str(e)}',
                'provider': 'vultr'
            }
        except Exception as e:
            return {
                'error': f'列出Vultr实例时发生错误: {str(e)}',
                'provider': 'vultr'
            }
  • Global instantiation of the VultrProvider class instance used by the tool handler.
    vultr_provider = VultrProvider() 
  • main.py:540-540 (registration)
    The @mcp.tool() decorator that registers the list_vultr_instances function as an MCP tool.
    @mcp.tool()
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does ('list all Vultr instances') without any behavioral details. It doesn't mention whether this is a read-only operation, what data is returned (e.g., instance IDs, statuses, metadata), pagination, rate limits, or error handling. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 ('列出所有Vultr实例') that directly states the tool's purpose without any wasted words. It's front-loaded and appropriately sized for a simple listing tool, 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?

Given that annotations are absent and the tool has an output schema (which handles return values), the description is incomplete. It lacks behavioral context (e.g., read-only nature, data format) and usage guidelines, which are crucial for an AI agent to invoke it correctly. While the output schema may cover return values, the description doesn't provide enough overall context for effective tool selection and use.

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

Parameters4/5

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

The input schema has 0 parameters with 100% description coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, but since there are no parameters, this is acceptable. A baseline score of 4 is appropriate as the description doesn't need to compensate for missing parameter documentation, and it aligns with the schema's lack of parameters.

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

Purpose3/5

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

The description '列出所有Vultr实例' (List all Vultr instances) clearly states the verb ('list') and resource ('Vultr instances'), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'list_aws_instances', 'list_alibaba_instances', or 'list_digitalocean_droplets' beyond specifying the provider, which is already implied by the tool name. The description is adequate but lacks differentiation from similar tools.

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. It doesn't mention when to choose this over 'get_vultr_instance_info' for detailed info on a single instance, or 'get_instance_by_provider' for cross-provider queries. There's no context about prerequisites, such as authentication or provider setup, leaving usage entirely implicit.

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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