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Wulnut

lark-agent

by Wulnut
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Python
Remote

get_task_detail

Retrieve complete details for a single work item, including all custom fields and user-friendly names. Ideal when you need a full overview of one task or issue.

Instructions

获取单个工作项的完整详情。

当你需要查看工作项的所有字段信息时使用此工具。
返回的详情包含所有可用字段（包括自定义字段）。
用户相关字段（如负责人、创建者等）会自动转换为人名以提高可读性。

Args:
    issue_id: 工作项 ID，必填。
    project: 项目标识符（可选）。可以是:
            - 项目名称（如 "SR6D2VA-7552-Lark"）
            - project_key（如 "project_xxx"）
            如不指定，则使用环境变量 FEISHU_PROJECT_KEY 配置的默认项目。
    work_item_type: 工作项类型名称（可选），如 "需求管理"、"Issue管理"、"项目管理" 等。
                   如不指定，默认使用项目中的第一个可用类型。
    user_key: (可选) 飞书用户标识符 (X-USER-KEY)，用于以特定用户身份进行操作。

Returns:
    JSON 格式的完整工作项详情。
    失败时返回错误信息。

Examples:
    # 获取工作项详情（使用默认项目）
    get_task_detail(issue_id=12345)

    # 指定项目和工作项类型
    get_task_detail(
        issue_id=12345,
        project="SR6D2VA-7552-Lark",
        work_item_type="Issue管理"
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectNo
issue_idYes
user_keyNo
work_item_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.2

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the return format (JSON full work item details), that all available fields including custom fields are returned, that user-related fields are converted to person names for readability, and that failures return error info. This is meaningful behavioral context beyond the name.

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 well-structured with Intro, Args, Returns, and Examples sections. It is slightly redundant in repeating 'complete details' and 'all fields', but every section earns its place and examples add practical value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists, the description does not need to enumerate the return structure. It covers all four parameters, their defaults, optionality, and real-world examples. The user_key behavior and default project resolution are also explained, making the tool complete for correct invocation.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully document the parameters. It does: issue_id is required, project accepts a name or project_key with defaulting behavior, work_item_type defaults to the first type, and user_key acts as the Feishu user identifier. Examples further clarify usage.

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

Purpose5/5

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

The description states a specific verb and resource: '获取单个工作项的完整详情' (get complete details of a single work item). It clearly differentiates from sibling get_tasks by emphasizing a single item and all fields, so an agent can distinguish it without ambiguity.

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 gives clear context: '当你需要查看工作项的所有字段信息时使用此工具' (use when you need to view all fields of a work item). It does not explicitly name alternatives or exclusion cases, but the usage context is sufficiently clear.

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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