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mcp-lark▌

aahl/skills · updated Apr 8, 2026

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$npx skills add https://github.com/aahl/skills --skill mcp-lark
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summary

Need to login to the Lark MCP Configuration Platform to add MCP services, obtain the MCP URL, and configure it into environment variables.

skill.md

MCP Lark / FeiShu

Need to login to the Lark MCP Configuration Platform to add MCP services, obtain the MCP URL, and configure it into environment variables.

Environment variables

Prioritize fetching from .env under the workspace, then use system environment variables. If not configured, ask the user for input and update it to .env.

# Configure multiple MCP service URLs and usage scenarios in environment variables
LARK_MCP_SERVERS='
open.larksuite.com/mcp/stream/xxx:Chats and Mails;
open.larksuite.com/mcp/stream/yyy:Cloud documents;
'

List of available tools

npx -y mcporter list 'open.larksuite.com/mcp/stream/<token>' --all-parameters

# Get the schema of the specified tool
npx -y mcporter list 'open.larksuite.com/mcp/stream/<token>' --json | jq '.tools[] | select(.name == "<tool_name>")'

Call specified tool

npx -y mcporter call 'open.larksuite.com/mcp/stream/<token>.<tool_name>' param1:"value1" foo:"bar"

References

  • Sent message content: references/message_create.md

About mcporter

To improve compatibility, use npx -y mcporter instead of mcporter when executing commands.

how to use mcp-lark

How to use mcp-lark 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 mcp-lark
2

Execute installation command

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

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

The skills CLI fetches mcp-lark from GitHub repository aahl/skills 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/mcp-lark

Reload or restart Cursor to activate mcp-lark. Access the skill through slash commands (e.g., /mcp-lark) 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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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)
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general reviews

Ratings

4.7★★★★★32 reviews
  • ★★★★★Anika Okafor· Dec 4, 2024

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

  • ★★★★★Ren Ramirez· Nov 23, 2024

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

  • ★★★★★Anika Wang· Oct 14, 2024

    Useful defaults in mcp-lark — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Oshnikdeep· Sep 21, 2024

    Solid pick for teams standardizing on skills: mcp-lark is focused, and the summary matches what you get after install.

  • ★★★★★Sophia Khanna· Sep 21, 2024

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

  • ★★★★★Hiroshi Bansal· Sep 5, 2024

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

  • ★★★★★Soo Nasser· Sep 1, 2024

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

  • ★★★★★Arya Sethi· Aug 24, 2024

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

  • ★★★★★Arjun Rao· Aug 20, 2024

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

  • ★★★★★Ganesh Mohane· Aug 12, 2024

    mcp-lark is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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