by roland0511
Seamlessly connect to Feishu Project management API for direct access to tasks, data, and resources using secure environ
Connects to Feishu project management platform to automate requirement analysis, bug tracking, and development workflow management. Provides end-to-end automation from requirement gathering to code commits.
Feishu Project is a community-built MCP server published by roland0511 that provides AI assistants with tools and capabilities via the Model Context Protocol. Seamlessly connect to Feishu Project management API for direct access to tasks, data, and resources using secure environ It is categorized under productivity.
You can install Feishu Project 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 Project 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
Feishu Project is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Feishu Project has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Useful MCP listing: Feishu Project is the kind of server we cite when onboarding engineers to host + tool permissions.
According to our notes, Feishu Project benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Feishu Project is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Feishu Project is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Feishu Project is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Feishu Project is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Feishu Project is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Strong directory entry: Feishu Project surfaces stars and publisher context so we could sanity-check maintenance before adopting.
showing 1-10 of 37
⚠️ 项目已归档 飞书项目已推出官方的MCP Server服务。
因此后续本仓库不再进行维护或更新。
请勿提交新的 Issue、Pull Request 或修改请求。
若需参考历史内容，可在只读模式下浏览本仓库。👉 建议查看飞书项目官方文档以获得最新的支持。
基于MCP（Model Context Protocol）协议的飞书项目管理工具，允许AI助手通过MCP协议与飞书项目管理系统进行交互。
本项目是一个MCP服务器实现，它封装了飞书项目管理的Open API，使AI助手能够获取飞书项目的视图列表、视图详情等信息。通过这个工具，AI助手可以帮助用户管理和查询飞书项目中的工作项。
在支持MCP协议的客户端（如Claude桌面客户端,Cursor,Cline等）的配置文件中添加本服务器。
更多MCP客户端可参考：https://modelcontextprotocol.io/clients
以Claude桌面客户端为例，编辑claude_desktop_config.json文件:
在mcpServers字段中添加以下配置：
{
"mcpServers": {
"feishuproj": {
"command": "uvx",
"args": ["mcp-feishu-proj@latest","--transport", "stdio"],
"env": {
"FS_PROJ_PROJECT_KEY": "your_project_key",
"FS_PROJ_USER_KEY": "your_user_key",
"FS_PROJ_PLUGIN_ID": "your_plugin_id",
"FS_PROJ_PLUGIN_SECRET": "your_plugin_secret"
}
}
}
}
git clone https://github.com/yourusername/mcp-feishu-proj.git
cd mcp-feishu-proj
# 安装uv（如果尚未安装）
pip install uv
# 创建虚拟环境并安装依赖
uv venv
uv pip install -e .
cp .env.example .env
.env文件，填入以下必要的配置信息：FS_PROJ_BASE_URL=https://project.feishu.cn/
FS_PROJ_PROJECT_KEY=your_project_key
FS_PROJ_USER_KEY=your_user_key
FS_PROJ_PLUGIN_ID=your_plugin_id
FS_PROJ_PLUGIN_SECRET=your_plugin_secret
其中：
FS_PROJ_BASE_URL：飞书项目API的基础URL，默认为https://project.feishu.cn/FS_PROJ_PROJECT_KEY：飞书项目的标识FS_PROJ_USER_KEY：用户标识FS_PROJ_PLUGIN_ID：飞书项目Open API的插件IDFS_PROJ_PLUGIN_SECRET：飞书项目Open API的插件密钥要添加新的飞书项目API功能，请按照以下步骤操作：
fsprojclient.py中添加新的API方法server.py中使用@mcp.tool装饰器注册新的MCP工具本项目包含了飞书项目Open API的Postman集合，位于docs/open-api-postman目录下，将目录下文件导入Postman可以进行快速调试飞书项目接口：
postman_environment.json：Postman环境变量配置postman_collection.json：Postman API集合本项目提供了Docker部署支持，可以通过Docker容器运行MCP飞书项目服务。
.env文件，设置必要的环境变量cp .env.example .env
然后编辑.env文件，填入你的飞书项目相关信息：
FS_PROJ_BASE_URL=https://project.feishu.cn/
FS_PROJ_PROJECT_KEY=your_project_key
FS_PROJ_USER_KEY=your_user_key
FS_PROJ_PLUGIN_ID=your_plugin_id
FS_PROJ_PLUGIN_SECRET=your_plugin_secret
docker-compose -f docker/docker-compose.yml up -d
这将使用ghcr.io/astral-sh/uv镜像，并挂载项目根目录到容器中，直接运行本地代码，便于开发和调试。Docker Compose会自动加载项目根目录中的.env文件作为环境变量。
docker-compose -f docker/docker-compose.yml logs -f
docker-compose -f docker/docker-compose.yml down
更多详细信息请参阅Docker部署文档。
首先，需要创建包含敏感信息的Secret。由于Kubernetes Secret需要使用base64编码的值，您需要对敏感信息进行编码：
# 对敏感信息进行base64编码
echo -n "your_project_key" | base64
echo -n "your_user_key" | base64
echo -n "your_plugin_id" | base64
echo -n "your_plugin_secret" | base64
然后，使用生成的base64编码值更新k8s-secret.yaml文件中的相应字段。
依次应用以下配置文件：
# 创建ConfigMap
kubectl apply -f k8s-configmap.yaml
# 创建Secret
kubectl apply -f k8s-secret.yaml
# 创建Deployment
kubectl apply -f k8s-deployment.yaml
检查部署状态：
# 查看Deployment状态
kubectl get deployments
# 查看Pod状态
kubectl get pods
# 查看Pod日志
kubectl logs -f <pod-name>
更多详细信息请参阅Kubernetes部署文档。
欢迎贡献代码、报告问题或提出改进建议。请遵循以下步骤：
git checkout -b feature/amazing-feature)git commit -m 'Add some amazing feature')git push origin feature/amazing-feature)本项目采用MIT许可证。详情请参阅LICENSE文件。
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.