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Naver Mail MCP Server

by Leonamin

list_mails

Retrieve recent emails from your Naver Mail account in JSON or text format, specifying the number of messages to fetch for mail management.

Instructions

최근 N개 메일 목록 조회 (JSON 또는 텍스트 형태)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_countNo가져올 메일 개수
formatNo출력 형태 (json: JSON 형태, text: 읽기 쉬운 텍스트(내용은 없음))text

Implementation Reference

  • The handler logic for "list_mails" tool inside 'handle_call_tool' in 'server.py'. It uses 'MailService.get_mails' to fetch data and then formats it as JSON or text.
    if name == "list_mails":
        max_count = args.get("max_count", 10)
        output_format = args.get("format", "text")
    
        mails = mail_service.get_mails(max_count=max_count)
    
        if output_format == "json":
            content = mails_to_json(mails)
        else:
            content = mails_to_text(mails)
    
        return [TextContent(type="text", text=content)]
  • server.py:30-53 (registration)
    The registration of "list_mails" tool within the '@server.list_tools()' decorated function in 'server.py'.
    @server.list_tools()
    async def handle_list_tools() -> list[Tool]:
        return [
            Tool(
                name="list_mails",
                description="최근 N개 메일 목록 조회 (JSON 또는 텍스트 형태)",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "max_count": {
                            "type": "number",
                            "default": 10,
                            "description": "가져올 메일 개수"
                        },
                        "format": {
                            "type": "string",
                            "enum": ["json", "text"],
                            "default": "text",
                            "description": "출력 형태 (json: JSON 형태, text: 읽기 쉬운 텍스트(내용은 없음))"
                        }
                    },
                    "required": [],
                }
            ),
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 of behavioral disclosure. It mentions the output format options (JSON or text) but lacks details on permissions, rate limits, error handling, or what 'recent' means (e.g., time frame, sorting). This leaves significant gaps for a tool that likely interacts with email data.

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 concise and front-loaded, stating the core functionality in a single sentence. However, it could be slightly improved by structuring it to highlight key points more clearly, such as separating format implications, but overall it is efficient with no wasted words.

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 lack of annotations and output schema, the description is incomplete. It does not explain the return values, error conditions, or behavioral traits like data freshness or access controls. For a tool with potential complexity in email retrieval, this leaves the agent with insufficient context to use it effectively.

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%, so the schema fully documents the parameters. The description adds minimal value by mentioning the format options but does not provide additional semantic context beyond what the schema already specifies, such as implications of choosing 'text' over 'json'.

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 tool's purpose as retrieving a list of recent emails with a specified count and format. It uses specific verbs ('최근 N개 메일 목록 조회') and identifies the resource ('메일'), but does not explicitly differentiate from sibling tools like 'list_mails_paginated' or 'get_mail_detail', which prevents a score of 5.

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 does not mention sibling tools such as 'list_mails_paginated' for paginated results or 'get_mail_detail' for detailed views, nor does it specify contexts or exclusions for usage.

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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