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ABLESTACK MOLD MCP Server

by ycyun

VM 목록 조회

mold_listVirtualMachines

Retrieve and filter virtual machines in ABLESTACK MOLD by criteria like name, state, zone, or project to manage cloud infrastructure.

Instructions

listVirtualMachines(4.21) 호출. 주요 필터만 노출(추가 필드는 mold_call 사용).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordNo
idNo
nameNo
stateNo
zoneidNo
projectidNo
domainidNo
accountNo
listallNo
detailsNo
pageNo
pagesizeNo

Implementation Reference

  • Handler function for mold_listVirtualMachines tool that invokes the listVirtualMachines API via callApi and returns JSON stringified response.
    async (args) => {
      const data = await callApi("listVirtualMachines", args);
      return { content: [{ type: "text", text: JSON.stringify(data, null, 2) }] };
    }
  • Input schema using Zod for validating parameters like keyword, id, name, state, etc., specific to listVirtualMachines API.
      inputSchema: {
        keyword: z.string().optional(),
        id: z.string().optional(),
        name: z.string().optional(),
        state: z.enum(["Running", "Stopped", "Present", "Destroyed", "Expunged"]).optional(),
        zoneid: z.string().optional(),
        projectid: z.string().optional(),
        domainid: z.string().optional(),
        account: z.string().optional(),
        listall: z.boolean().optional(),
        details: z.string().optional(),
        page: z.number().int().optional(),
        pagesize: z.number().int().optional(),
      },
    },
  • Registration of the mold_listVirtualMachines MCP tool, including title, description, input schema, and handler function.
    server.registerTool(
      "mold_listVirtualMachines",
      {
        title: "VM 목록 조회",
        description: "listVirtualMachines(4.21) 호출. 주요 필터만 노출(추가 필드는 mold_call 사용).",
        inputSchema: {
          keyword: z.string().optional(),
          id: z.string().optional(),
          name: z.string().optional(),
          state: z.enum(["Running", "Stopped", "Present", "Destroyed", "Expunged"]).optional(),
          zoneid: z.string().optional(),
          projectid: z.string().optional(),
          domainid: z.string().optional(),
          account: z.string().optional(),
          listall: z.boolean().optional(),
          details: z.string().optional(),
          page: z.number().int().optional(),
          pagesize: z.number().int().optional(),
        },
      },
      async (args) => {
        const data = await callApi("listVirtualMachines", args);
        return { content: [{ type: "text", text: JSON.stringify(data, null, 2) }] };
      }
    );
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool exposes only major filters and references version '4.21', but doesn't describe what the tool actually returns, whether it's paginated (despite page/pagesize parameters), authentication requirements, rate limits, or error behavior. The description provides minimal behavioral context beyond the basic operation.

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

Conciseness4/5

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

The description is extremely concise - just two short sentences in Korean. It's front-loaded with the core operation and immediately provides the key alternative guidance. While efficient, it may be too terse given the complexity of the tool with 12 undocumented parameters.

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 12 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how to interpret results, what the parameters do, or the behavioral characteristics. The mention of version '4.21' and the mold_call alternative provides minimal context but leaves most questions unanswered.

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?

With 0% schema description coverage for 12 parameters, the description provides almost no parameter semantics. It mentions '주요 필터만 노출' (only major filters exposed) which hints at filtering capabilities, but doesn't explain what any of the 12 parameters mean, how they interact, or which are the 'major filters'. The description fails to compensate for the complete lack of schema documentation.

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 states the tool calls 'listVirtualMachines(4.21)' which implies listing virtual machines, but it's vague about what this actually does. It mentions '주요 필터만 노출' (only major filters exposed) but doesn't clearly explain what the tool returns or its scope. While it distinguishes from 'mold_call' for additional fields, the core purpose remains somewhat ambiguous.

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

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear guidance on when to use alternatives: '추가 필드는 mold_call 사용' (use mold_call for additional fields). This explicitly tells the agent to use mold_call when needing fields beyond the major filters. However, it doesn't specify when to use this tool versus other VM-related siblings like mold_startVirtualMachine or mold_stopVirtualMachine.

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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