lark-mcp

whatevertogo/feishuskill · updated Apr 8, 2026

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$npx skills add https://github.com/whatevertogo/feishuskill --skill lark-mcp
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skill.md

Lark MCP

⚠️ 重要提醒

搜索文档/知识库必须配置 OAuth

  • docx_builtin_search → 需要 --oauth
  • wiki_v1_node_search → 需要 --oauth

否则返回 99991663 错误。配置方法见 installation.md


核心规则

# 工具命名(连字符,非下划线)
✅ mcp__lark-mcp__tool_name
❌ mcp__lark_mcp__tool_name

# 参数结构
path: {app_token, table_id}   # URL路径参数
params: {page_size, ...}      # 查询参数
data: {fields, ...}           # 请求体
useUAT: false                 # true=用户身份, false=租户身份

常见陷阱

# content 必须是 JSON 字符串
❌ content: {"text": "hello"}
✅ content: '{"text": "hello"}'

# 过滤条件 value 必须是数组
❌ value: "已完成"
✅ value: ["已完成"]

# 创建群组必须指定 owner_id,否则群主为机器人
owner_id: "ou_xxxxx"

# 参数名差异
docx_builtin_search: search_key  # 不是 query
wiki_v1_node_search: query       # 不是 search_key

# token 类型
wiki_v2_space_getNode: 用 wikcn...  # 不能用 doxcn...
docx_v1_document_rawContent: 用 doxcn...

useUAT 选择

场景 useUAT
创建资源(想让用户可访问) true
搜索文档/知识库 true
访问用户私有数据 true
查询公共数据 false

工具速查

类别 工具 文档
消息 im_v1_message_create, im_v1_message_list im.md
群组 im_v1_chat_create, im_v1_chat_list, im_v1_chatMembers_get chat.md
多维表格 bitable_v1_app_create, bitable_v1_appTableRecord_search/create/update bitable.md
文档 docx_builtin_search, docx_v1_document_rawContent, docx_builtin_import documents.md
知识库 wiki_v1_node_search, wiki_v2_space_getNode wiki.md

ID 类型

前缀 类型 来源
ou_ 用户ID API返回
oc_ 群聊ID im_v1_chat_list
bascn 多维表格 URL中 base/
tbl 数据表 URL参数 table=
doxcn 文档 搜索结果或URL
wikcn 知识库节点 知识库URL

快速示例

# 发送消息
工具: mcp__lark-mcp__im_v1_message_create
data:
  receive_id: "oc_xxxxx"
  msg_type: "text"
  content: '{"text": "消息内容"}'
params:
  receive_id_type: "chat_id"

# 创建群组
工具: mcp__lark-mcp__im_v1_chat_create
data:
  name: "群名"
  chat_mode: "group"
  owner_id: "ou_xxxxx"
  user_id_list: ["ou_xxxxx"]
params:
  user_id_type: "open_id"

# 创建多维表格记录
工具: mcp__lark-mcp__bitable_v1_appTableRecord_create
path:
  app_token: "bascnxxxxxx"
  table_id: "tblxxxxxx"
data:
  fields:
    文本字段: "值"
    单选字段: "选项名"
useUAT: true

# 搜索文档
工具: mcp__lark-mcp__docx_builtin_search
data:
  search_key: "关键词"
  count: 10
useUAT: true

错误速查

错误 原因 解决
tool not found 服务器名错误 使用 mcp__lark-mcp__ 前缀
99991663 权限不足 useUAT: true 或配置 OAuth
131005 not found token 类型错误 检查用 wikcn 还是 doxcn
创建资源无法访问 租户身份创建 使用 useUAT: true
field not found 字段名错误 appTableField_list 确认
invalid content 格式错误 content 用单引号包裹 JSON

详细文档: troubleshooting.md | installation.md

how to use lark-mcp

How to use lark-mcp on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add lark-mcp
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/whatevertogo/feishuskill --skill lark-mcp

The skills CLI fetches lark-mcp from GitHub repository whatevertogo/feishuskill and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/lark-mcp

Reload or restart Cursor to activate lark-mcp. Access the skill through slash commands (e.g., /lark-mcp) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.666 reviews
  • Carlos Harris· Dec 28, 2024

    I recommend lark-mcp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ishan Bhatia· Dec 28, 2024

    lark-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Ishan Khan· Dec 28, 2024

    lark-mcp reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Carlos Martin· Dec 20, 2024

    Registry listing for lark-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ganesh Mohane· Dec 16, 2024

    lark-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Alexander White· Dec 4, 2024

    I recommend lark-mcp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Diego Bhatia· Nov 23, 2024

    Keeps context tight: lark-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Valentina Ghosh· Nov 19, 2024

    We added lark-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Harper Zhang· Nov 11, 2024

    lark-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sakshi Patil· Nov 7, 2024

    Registry listing for lark-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.

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