feishu-mcp

feishu-mcp

Enables AI agents to control Feishu (Lark) through structured tools for messaging, calendar, tasks, and documents.

Category
Visit Server

README

feishu-miqroera-mcp

πŸ€– Feishu MCP Server β€” Let AI Agents directly control Feishu: send messages, create calendars, manage tasks, and write cloud documents.

Python License: MIT MCP


Table of Contents


Features

This project implements an MCP (Model Context Protocol) Server that wraps Feishu’s core capabilities as structured AI tools, enabling any MCP-compatible AI Agent / LLM toolchain to:

Capability Functions
πŸ’¬ Messages Send & reply to group messages, @mention members, Markdown format
πŸ“… Calendar Create/query group calendars, create events, invite attendees
βœ… Tasks Create tasks, assign owners, set due dates
πŸ“„ Documents Create cloud docs, write Markdown content, upload files, one-click share links, set collaborator permissions
πŸ‘₯ Users Get group member lists, resolve users by name
πŸ”” Events Receive Feishu push events via long-connection WebSocket in real time (no public IP required)

Quick Start

1. Clone and install

git clone https://github.com/your-username/feishu-miqroera-mcp.git
cd feishu-miqroera-mcp

python -m venv .venv

# Windows
.venv\Scripts\activate
# macOS / Linux
source .venv/bin/activate

pip install -e .

2. Configure Feishu app credentials

cp .env.example .env

Edit .env:

FEISHU_APP_ID=cli_xxxxxxxxxxxxxxxxxx
FEISHU_APP_SECRET=xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

How to get credentials: log in to the Feishu Open Platform β†’ create an in-house app β†’ go to the "Credentials & Basic Info" page

3. Verify the installation

# Confirm the MCP server starts correctly (Ctrl+C to exit)
.venv\Scripts\python.exe -m feishu_mcp.server

You should see MCP server running on stdio indicating success.


Connect an AI Agent

NanoBot (recommended)

NanoBot is a lightweight multimodal AI Agent framework with native support for MCP Servers and Feishu channels.

Step 1: Edit the NanoBot config file

# Config file location (auto-created)
~/.nanobot/config.json        # macOS / Linux
%USERPROFILE%\.nanobot\config.json  # Windows

Step 2: Add feishu-mcp to tools.mcpServers

{
  "tools": {
    "mcpServers": {
      "feishu-mcp": {
        "command": "C:/path/to/feishu-miqroera-mcp/.venv/Scripts/python.exe",
        "args": ["-m", "feishu_mcp.server"],
        "env": {
          "FEISHU_APP_ID": "cli_xxxxxxxxxxxxxxxxxx",
          "FEISHU_APP_SECRET": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
        }
      }
    }
  },
  "channels": {
    "feishu": {
      "appId": "cli_xxxxxxxxxxxxxxxxxx",
      "appSecret": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
    }
  }
}

Windows note: Use forward slashes / or double backslashes \\ in paths

Step 3: Start NanoBot

nanobot run

NanoBot will automatically establish a Feishu long connection; the AI can then drive Feishu operations via natural language.


Claude Desktop

Step 1: Locate the config file

OS Path
Windows %APPDATA%\Claude\claude_desktop_config.json
macOS ~/Library/Application Support/Claude/claude_desktop_config.json

Step 2: Add the MCP Server config

{
  "mcpServers": {
    "feishu-mcp": {
      "command": "C:/path/to/feishu-miqroera-mcp/.venv/Scripts/python.exe",
      "args": ["-m", "feishu_mcp.server"],
      "env": {
        "FEISHU_APP_ID": "cli_xxxxxxxxxxxxxxxxxx",
        "FEISHU_APP_SECRET": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
      }
    }
  }
}

macOS example: "command": "/Users/yourname/feishu-miqroera-mcp/.venv/bin/python"

Step 3: Restart Claude Desktop

After restarting, look for the πŸ”§ icon in the chat input area and confirm "feishu-mcp" is loaded.


Cursor

Step 1: Locate the config file

<project root>/.cursor/mcp.json

Or global config:

OS Path
Windows %USERPROFILE%\.cursor\mcp.json
macOS ~/.cursor/mcp.json

Step 2: Add config

{
  "mcpServers": {
    "feishu-mcp": {
      "command": "C:/path/to/feishu-miqroera-mcp/.venv/Scripts/python.exe",
      "args": ["-m", "feishu_mcp.server"],
      "env": {
        "FEISHU_APP_ID": "cli_xxxxxxxxxxxxxxxxxx",
        "FEISHU_APP_SECRET": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
      }
    }
  }
}

Step 3: Open Cursor β†’ Settings β†’ MCP β†’ Confirm feishu-mcp status is green


Cline / Continue / Other MCP-compatible tools

All tools that support MCP stdio transport can connect using the same config format:

{
  "command": "/absolute/path/to/.venv/bin/python",
  "args": ["-m", "feishu_mcp.server"],
  "env": {
    "FEISHU_APP_ID": "cli_xxx",
    "FEISHU_APP_SECRET": "xxx"
  }
}

Long-Connection Event Listener

Feishu's long connection (WebSocket) requires no public IP and no ngrok β€” receive Feishu push events directly from behind NAT.

# Start the event listener standalone (runs 24/7)
.venv\Scripts\python.exe -m feishu_mcp.webhook.longconn

Example log output after startup:

INFO  Connecting to Feishu WebSocket: wss://msg-frontier.feishu.cn/ws/v2
INFO  Feishu long connection established
INFO  Received message event: chat_id=oc_xxx, sender=ou_xxx, text=Hello

Receiving events in your own application code:

import lark_oapi as lark

client = lark.Client.builder() \
    .app_id("cli_xxx") \
    .app_secret("xxx") \
    .event_callback(lark.EventType.IM_MESSAGE_RECEIVE_V1, your_handler) \
    .build()

ws = lark.ws.Client(app_id, app_secret, event_handler=client.event_handler)
ws.start()

Docker Deployment

Using Docker directly

# Build image
docker build -t feishu-miqroera-mcp .

# Start Feishu event listener (background)
docker run -d \
  --name feishu-listener \
  --env-file .env \
  --restart unless-stopped \
  feishu-miqroera-mcp \
  feishu_mcp.webhook.longconn

# View logs
docker logs -f feishu-listener

Using Docker Compose

# Copy and fill in env vars
cp .env.example .env
# Edit .env with APP_ID and APP_SECRET

# Start event listener
docker compose up -d feishu-listener

# Check status
docker compose ps

# View logs
docker compose logs -f feishu-listener

MCP Server (stdio) is usually invoked directly as a local process by the AI Agent framework and does not need to be containerized.
Docker is primarily used for persistently running the event listener.


Full Tool List

Tool Description
get_chat_members Get the member list of a group
resolve_users_by_name Look up users in a group by name
send_message Send a text/rich-text message to a group or user
reply_message Reply to a specific message
get_or_create_group_calendar Get or create a shared group calendar
create_calendar_event Create an event in a calendar
add_event_attendees Add attendees to a calendar event
list_calendar_events Query the list of calendar events
create_task Create a Feishu task (with due time and description)
assign_task Assign an owner to a task
add_task_to_list Add a task to a tasklist
list_tasks Query the task list
create_folder Create a folder in Drive
create_document Create a Feishu cloud document
write_document_markdown Write Markdown content into a document (accepts docx document_id or wiki node_token)
upload_file Upload a file to Feishu Drive
upload_file_and_share Upload a file and return a shareable link in one step (upload + set permission + get link)
insert_file_block Insert a file attachment block into a document (accepts docx document_id or wiki node_token)
set_doc_permission Add collaborators to a document (supports users or groups)
set_doc_public_access Set document public access / link sharing permission
get_share_link Get the sharing link for a document
grant_permission_request Handle a permission request and authorize the applicant

See docs/api.md for full parameter details.


Feishu App Permissions

Enable the following permissions in your app's management page on the Feishu Developer Console:

Permission Purpose
im:message Send/receive messages
im:message.group_at_msg Group @mention feature
im:chat.members:read Read group members
task:task Task read/write
calendar:calendar Calendar read/write
drive:drive Drive/document read/write
docx:document Cloud document content editing
wiki:wiki:readonly Resolve wiki node tokens to docx document IDs (required if documents are wiki-mounted)

Path to enable permissions: App Management β†’ Permission Management β†’ Enable the above permissions β†’ Publish version.


Development & Contributing

# Run unit tests
pytest tests/ -v

# Run integration tests (requires real Feishu credentials)
pytest tests/integration/ -v

# Format code
ruff format src/ tests/

# Lint check
ruff check src/ tests/

See CONTRIBUTING.md for details.


License

MIT Β© 2026 feishu-miqroera-mcp contributors

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured