by larksuite
Integrate AI assistants with Feishu/Lark's collaboration tools using the official Node.js SDK for seamless productivity
Connects AI coding tools to Feishu (Lark) workspace documents, enabling direct access, editing, and search of collaborative documents. Supports document creation, content modification, and folder browsing within your development workflow.
Feishu/Lark is an official MCP server published by larksuite that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate AI assistants with Feishu/Lark's collaboration tools using the official Node.js SDK for seamless productivity It is categorized under productivity, communication.
You can install Feishu/Lark in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Feishu/Lark is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
I recommend Feishu/Lark for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Feishu/Lark benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Feishu/Lark reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Useful MCP listing: Feishu/Lark is the kind of server we cite when onboarding engineers to host + tool permissions.
Strong directory entry: Feishu/Lark surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We evaluated Feishu/Lark against two servers with overlapping tools; this profile had the clearer scope statement.
Feishu/Lark has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Useful MCP listing: Feishu/Lark is the kind of server we cite when onboarding engineers to host + tool permissions.
Feishu/Lark reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Feishu/Lark is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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Developer Documentation Retrieval MCP
⚠️ Beta Version Notice: This tool is currently in Beta stage. Features and APIs may change, so please stay updated with version releases.
This is the Feishu/Lark official OpenAPI MCP (Model Context Protocol) tool designed to help users quickly connect to the Feishu/Lark platform and enable efficient collaboration between AI Agents and Feishu/Lark. The tool encapsulates Feishu/Lark Open Platform API interfaces as MCP tools, allowing AI assistants to directly call these interfaces and implement various automation scenarios such as document processing, conversation management, calendar scheduling, and more.
Before using the lark-mcp tool, you need to create a Feishu/Lark application:
For detailed application creation and configuration guidelines, please refer to the Feishu Open Platform Documentation - Creating an Application.
Before using the lark-mcp tool, you need to install the Node.js environment.
Using the Official Installer (Recommended):
node -v
npm -v
To integrate Feishu/Lark functionality in AI tools like Trae, Cursor or Claude, install using the button below.
or add the following to your configuration file:
{
"mcpServers": {
"lark-mcp": {
"command": "npx",
"args": [
"-y",
"@larksuiteoapi/lark-mcp",
"mcp",
"-a",
"<your_app_id>",
"-s",
"<your_app_secret>"
]
}
}
}
If you need to access APIs with user identity, you need to login first using the login command in the terminal. Note that you need to configure the application's redirect URL in the developer console first, default is http://localhost:3000/callback
# Login and get user access token
npx -y @larksuiteoapi/lark-mcp login -a cli_xxxx -s yyyyy
# Or optionally, login with specific OAuth scope - if not specified, all permissions will be authorized by default
npx -y @larksuiteoapi/lark-mcp login -a cli_xxxx -s yyyyy --scope offline_access docx:document
Then add the following to your configuration file:
{
"mcpServers": {
"lark-mcp": {
"command": "npx",
"args": [
"-y",
"@larksuiteoapi/lark-mcp",
"mcp",
"-a",
"<your_app_id>",
"-s",
"<your_app_secret>",
"--oauth",
"--token-mode", "user_access_token"
]
}
}
}
Note: When enabling --oauth, it's recommended to explicitly set --token-mode to user_access_token, which means calling APIs with user access tokens, suitable for accessing user resources or scenarios requiring user authorization (such as reading personal documents, sending IM messages). If you keep the default auto, some APIs AI may fallback to tenant_access_token, which could result in insufficient permissions or inability to access user private data.
Based on your usage scenario, lark-mcp supports configuring different domain environments:
Feishu (China Version):
https://open.feishu.cn domainLark (International Version):
https://open.larksuite.com domainTo switch to the international version of Lark, add the --domain parameter in your configuration:
{
"mcpServers": {
"lark-mcp": {
"command": "npx",
"args": [
"-y",
"@larksuiteoapi/lark-mcp",
"mcp",
"-a",
"<your_app_id>",
"-s",
"<your_app_secret>",
"--domain",
"https://open.larksuite.com"
]
}
}
}
💡 Tip: Ensure your application is created in the corresponding domain environment's open platform. International version applications cannot be used with Feishu China version, and vice versa.
⚠️ File Upload/Download: File upload and download operations are not yet supported
⚠️ Document Editing: Direct editing of Feishu cloud documents is not supported (only importing and reading are available)
By default, the MCP service enables common APIs. To enable other tools or only specific APIs or presets, you can specify them using the -t parameter in the MCP Client configuration (JSON):
{
"mcpServers": {
"lark-mcp": {
"command": "npx",
"args": [
"-y",
"@larksuiteoapi/lark-mcp",
"mcp",
"-a", "<your_app_id>",
"-s", "<your_app_secret>",
"-t", "im.v1.message.create,im.v1.message.list,im.v1.chat.create,preset.calendar.default"
]
}
}
}
For detailed information about all preset tool collections and which tools are included in each preset, please refer to the Preset Tool Collections Reference.
A complete list of all supported Feishu/Lark tools can be found in tools.md.
⚠️ Notice:Non-preset APIs have not undergone compatibility testing, and the AI may not perform optimally during the process of understanding and using them.
Developers can refer to the minimal example for integrating with Agent: lark-samples/mcp_quick_demo.
You can also refer to the Lark bot integration example: lark-samples/mcp_larkbot_demo/nodejs.
This example demonstrates how to integrate MCP capabilities into Feishu/Lark bots, triggering tool calls and message sending through bot conversations, suitable for scenarios that integrate existing tools into Bot.
For detailed configuration options and deployment scenarios, please refer to our Configuration Guide.
For detailed information about all available command line parameters and their usage, please refer to the Command Line Reference.
Issues are welcome to help improve this tool. If you have any questions or suggestions, please raise them in the GitHub repository.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.