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rainhan99

Cloud Manage MCP Server

by rainhan99

get_digitalocean_droplet_actions

Retrieve the action history for a DigitalOcean Droplet to monitor operations, track changes, and audit management activities on your cloud server.

Instructions

获取DigitalOcean Droplet操作历史

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
droplet_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:475-479 (handler)
    MCP tool handler for get_digitalocean_droplet_actions, decorated with @mcp.tool(). Delegates to digitalocean_provider.get_droplet_actions.
    def get_digitalocean_droplet_actions(droplet_id: int) -> Dict:
        """
        获取DigitalOcean Droplet操作历史
        """
        return digitalocean_provider.get_droplet_actions(droplet_id)
  • Core implementation of the droplet actions retrieval in DigitalOceanProvider class using pydo Client to list droplet actions.
    def get_droplet_actions(self, droplet_id: int) -> Dict:
        """
        获取Droplet的操作历史
        
        Args:
            droplet_id (int): Droplet ID
            
        Returns:
            Dict: 操作历史或错误信息
        """
        if not self.available:
            return {
                'error': f'DigitalOcean服务不可用: {getattr(self, "error", "未知错误")}',
                'provider': 'digitalocean'
            }
        
        try:
            response = self.client.droplet_actions.list(droplet_id=droplet_id)
            actions = response.get("actions", [])
            
            action_list = []
            for action in actions:
                action_info = {
                    'id': action.get("id"),
                    'status': action.get("status"),
                    'type': action.get("type"),
                    'started_at': action.get("started_at"),
                    'completed_at': action.get("completed_at"),
                    'resource_id': action.get("resource_id"),
                    'resource_type': action.get("resource_type"),
                    'region': action.get("region", {}).get("name", "未知")
                }
                action_list.append(action_info)
            
            return {
                'provider': 'digitalocean',
                'droplet_id': droplet_id,
                'total_actions': len(action_list),
                'actions': action_list
            }
            
        except Exception as e:
            return {
                'error': f'获取Droplet操作历史时发生错误: {str(e)}',
                'provider': 'digitalocean'
            }
Behavior1/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It only states what the tool does ('获取...操作历史'), without any information on permissions, rate limits, response format, or side effects. This is a significant gap for a tool that likely involves read operations on a cloud resource.

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 in Chinese ('获取DigitalOcean Droplet操作历史'), which directly states the purpose without unnecessary words. It is appropriately sized and front-loaded, 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 the tool's complexity (interacting with a cloud provider), lack of annotations, and 0% schema description coverage, the description is incomplete. While an output schema exists, the description doesn't compensate for missing behavioral and parameter details, making it inadequate for safe and effective use.

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

Parameters2/5

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

Schema description coverage is 0%, with one parameter 'droplet_id' documented only by title and type in the schema. The description adds no meaning beyond the schema, failing to explain what 'droplet_id' represents or how to obtain it, which is critical for correct invocation.

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 ('获取' meaning 'get') and resource ('DigitalOcean Droplet操作历史' meaning 'DigitalOcean Droplet operation history'), making the purpose specific and understandable. It distinguishes from siblings like 'get_digitalocean_droplet_info' by focusing on actions/history rather than general information, though it doesn't explicitly name alternatives.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions, leaving the agent to infer usage from the purpose alone, which is insufficient for effective tool selection.

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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