Feishu Project▌
by roland0511
Seamlessly connect to Feishu Project management API for direct access to tasks, data, and resources using secure environ
Integrates with Feishu Project management API, enabling direct interaction with project data, tasks, and resources through environment variable authentication and stdio-based transport.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Development teams using Feishu for project management
- / Automating software development workflows
- / Quality control and requirement completeness validation
- / Chinese-speaking teams with Feishu infrastructure
capabilities
- / Extract and analyze requirements from Feishu projects
- / Generate technical specifications and architecture designs
- / Track bugs and manage development tasks
- / Update project status and task states automatically
- / Integrate with GitLab for automated code commits
- / Monitor system health across components
what it does
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.
about
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.
how to install
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.
license
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.
readme
⚠️ 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。
MCP-飞书项目管理工具
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。
项目简介
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。
使用方法
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:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json
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"
}
}
}
}
已支持功能(欢迎贡献)
登录认证
- 登录及认证流程
视图功能
- 获取飞书项目视图列表
- 获取视图工作项列表
- 创建固定视图
- 更新固定视图
- 创建条件视图
- 更新条件视图
- 删除视图
工作项管理
- 获取工作项详情
- 获取创建工作项元数据
- 创建工作项
- 更新工作项
- 批量更新工作项字段值
- 删除工作项
- 终止/恢复工作项
- 获取工作项操作记录
工作项搜索
- 获取指定的工作项列表(单空间)
- 获取指定的工作项列表(跨空间)
- 获取指定的工作项列表(单空间-复杂传参)
- 获取指定的工作项列表(全局搜索)
- 获取指定的关联工作项列表
附件管理
- 添加附件
- 文件上传
- 下载附件
- 删除附件
空间管理
- 获取空间列表
- 获取空间详情
- 获取空间下业务线详情
- 获取空间下工作项类型
- 获取空间下团队成员
角色与人员配置
- 获取流程角色配置详情
空间关联
- 获取空间关联规则列表
- 获取空间关联下的关联工作项实例列表
- 绑定空间关联的关联工作项实例
- 解绑空间关联的关联工作项实例
流程与节点
- 获取工作流详情
- 获取工作流详情(WBS)
- 更新节点/排期
- 节点完成/回滚
- 状态流转
流程配置
- 获取工作项下的流程模板列表
- 获取流程模板配置详情
- 新增流程模板
- 更新流程模板
- 删除流程模板
子任务
- 获取指定的子任务列表
- 获取子任务详情
- 创建子任务
- 更新子任务
- 子任务完成/回滚
- 删除子任务
评论
- 添加评论
- 查询评论
- 更新评论
- 删除评论
其他功能
- 拉机器人入群
- 获取度量图表明细数据
- 获取流程角色配置详情
开发指南
开发环境配置
- 克隆本仓库:
git clone https://github.com/yourusername/mcp-feishu-proj.git
cd mcp-feishu-proj
- 安装依赖(使用uv):
# 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 .
配置说明
- 复制环境变量示例文件并进行配置:
cp .env.example .env
- 编辑
.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
添加新功能
要添加新的飞书项目API功能,请按照以下步骤操作:
- 在
fsprojclient.py中添加新的API方法 - 在
server.py中使用@mcp.tool装饰器注册新的MCP工具
飞书项目Open API参考
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
容器化部署指南
Docker部署
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。
前提条件
- 安装 Docker
- 安装 Docker Compose
使用Docker Compose运行
- 创建
.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
- 使用Docker Compose启动服务
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。
- 查看日志
docker-compose -f docker/docker-compose.yml logs -f
- 停止服务
docker-compose -f docker/docker-compose.yml down
更多详细信息请参阅Docker部署文档。
Kubernetes部署
前提条件
- 一个可用的Kubernetes集群
- 已安装kubectl命令行工具
- 具有创建Deployment、ConfigMap和Secret的权限
部署步骤
- 准备Secret
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。
- 应用配置
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
- 验证部署
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。
贡献指南
欢迎贡献代码、报告问题或提出改进建议。请遵循以下步骤:
- Fork本仓库
- 创建您的特性分支 (
git checkout -b feature/amazing-feature) - 提交您的更改 (
git commit -m 'Add some amazing feature') - 推送到分支 (
git push origin feature/amazing-feature) - 创建一个Pull Request
许可证
本项目采用MIT许可证。详情请参阅LICENSE文件。
FAQ
- What is the Feishu Project MCP server?
- Feishu Project is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Feishu Project?
- This profile displays 37 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.4 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.4★★★★★37 reviews- ★★★★★Pratham Ware· Dec 28, 2024
Feishu Project is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Kaira Nasser· Dec 28, 2024
Feishu Project has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Harper Abbas· Dec 20, 2024
Useful MCP listing: Feishu Project is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Anika Chawla· Dec 20, 2024
According to our notes, Feishu Project benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Arjun Jain· Dec 8, 2024
Feishu Project is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Kaira Garcia· Dec 4, 2024
Feishu Project is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Alexander Lopez· Nov 27, 2024
Feishu Project is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Harper Singh· Nov 23, 2024
Feishu Project is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Sakshi Patil· Nov 19, 2024
Feishu Project is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Harper White· Nov 11, 2024
Strong directory entry: Feishu Project surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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