开源MCP工具 是 AI Skill Hub 本期精选MCP工具之一。综合评分 7.5 分,整体质量较高。我们推荐使用将其纳入你的 AI 工具库,帮助提升工作效率。
基于Python的开源MCP工具,用于会议准备,整合了公司和参与者研究笔记,突出其与G的集成
开源MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
基于Python的开源MCP工具,用于会议准备,整合了公司和参与者研究笔记,突出其与G的集成
开源MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/tavily-ai/meeting-prep-agent
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"--mcp--": {
"command": "npx",
"args": ["-y", "meeting-prep-agent"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 开源MCP工具 执行以下任务... Claude: [自动调用 开源MCP工具 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"__mcp__": {
"command": "npx",
"args": ["-y", "meeting-prep-agent"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
Download a full demo video by clicking here
Python version: 3.13.2 (local development)
See google-calendar-mcp for full details.
Google Cloud Setup: 1. Go to the Google Cloud Console and create/select a project. 2. Enable the Google Calendar API. 3. Create OAuth 2.0 credentials: - Go to Credentials - Click "Create Credentials" > "OAuth client ID" - Choose "User data" for the type of data that the app will be accessing - Add your app name and contact information - Select "Desktop app" as the application type 4. Add your email as a test user under the OAuth Consent screen. 5. Create a file gcp-oauth.keys.json in the root of google-calendar-mcp directory. 5. Download your credentials and paste them in gcp-oauth.keys.json.
This file should look like:
{
"installed": {
"client_id": "<your-client-id>",
"project_id": "<your-project-id>",
"auth_uri": "<your-auth-uri>",
"token_uri": "<your-token-uri>",
"auth_provider_x509_cert_url": "<your-auth-provider>",
"client_secret": "<your-secret>",
"redirect_uris": ["http://localhost"]
}
}
Install the MCP:
cd google-calendar-mcp
npm install
Set config path:
GOOGLE_CALENDAR_CONFIG=<absolute-path-to-project>/mcp-use-case/google-calendar-mcp/build/index.js Run the notebook mcp-test.ipynb to check that your MCP setup is working before proceeding.
1. Create and activate a virtual environment:
python3 -m venv venv
source venv/bin/activate # On Windows: .\venv\Scripts\activate
2. Install dependencies: python3 -m pip install -r requirements.txt
3. Set environment variables: export TAVILY_API_KEY="your-tavily-api-key"
export OPENAI_API_KEY="your-openai-api-key"
export GROQ_API_KEY="your-groq-api-key"
export GOOGLE_CALENDAR_CONFIG="<absolute-path-to-project>/mcp-use-case/google-calendar-mcp/build/index.js"
4. Run the backend server: python app.py
1. Navigate to the frontend directory:
cd ui
2. Install dependencies: npm install
3. Start the development server: npm run start
.env file example:
TAVILY_API_KEY=your-tavily-api-key
OPENAI_API_KEY=your-openai-api-key
GROQ_API_KEY=your-groq-api-key
GOOGLE_CALENDAR_CONFIG=your-google-config
---
By default, the agent prepares a meeting brief using public web research on attendees and the companies you are meeting with.
To ground the briefing in your real relationship with meeting attendees, enable iGPT. iGPT retrieves internal-only context from your connected email and returns meeting-relevant internal context passed into the LangGraph flow.
#### What changes when iGPT is available? A typical enriched brief may include: - What was last discussed with each attendee (internal threads) - Commitments made by either side (and what’s still pending) - Open items / unresolved questions
#### How iGPT is used in this agent The agent sends company and attendee information (derived from your Google Calendar events) to iGPT and asks it to retrieve relevant prior internal context.
Key rule enforced in the iGPT step: - Internal sources only (no public web information)
You need two things: 1) IGPT_API_KEY (created in the Hub) 2) IGPT_USER (the user value you choose inside the Playground)
#### 1) Create an API key - Go to: https://igpt.ai/hub/apikeys/ - Create a new key (treat this as a secret - do not share it)
#### 2) Connect your inbox (one-time per user) You can still connect your email through the Playground, even if you don’t have an iGPT app yet.
1. Open the Playground: - https://igpt.ai/hub/playground/ 2. Go to Connect Datasource 3. Choose a user value (this is your end-user identifier) - Example for a demo: demo_user 4. Submit the request - the Playground will return an OAuth link 5. Click the OAuth link and complete the login/consent 6. Indexing will start for that user
✅ Important: The user value you used in Connect Datasource is the same value you must use later when calling iGPT (and what you should place in IGPT_USER).
#### 3) Use the same user when querying iGPT In the Playground “Ask” screen (e.g. /v1/recall/ask) you will see a user field. That value must match the one you used when connecting the datasource.
#### Add to your .env Set IGPT_USER to the exact same user value you used in the Playground:
IGPT_API_KEY=your-igpt-api-key
IGPT_USER=your-playground-user-value
Links
POST /api/analyze-meetings: Handles streamed LangGraph execution---
该项目基于Python开发,提供了会议准备的开源MCP工具,整合了公司和参与者研究笔记,具有较好的可扩展性和易用性,但缺乏详细的文档和用户指南
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,开源MCP工具 在MCP工具赛道中表现稳健,质量良好。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | meeting-prep-agent |
| 原始描述 | 开源MCP工具:Prepare for meetings with company and attendee research notes, integrated with G。⭐194 · Python |
| Topics | mcppython |
| GitHub | https://github.com/tavily-ai/meeting-prep-agent |
| License | MIT |
| 语言 | Python |
收录时间:2026-06-10 · 更新时间:2026-06-10 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端