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google_workspace_mcp MCP工具

基于 Python · 让 AI 助手直接操作你的系统与工具
英文名:google_workspace_mcp
⭐ 2.4k Stars 🍴 729 Forks 💻 Python 📄 MIT 🏷 AI 8.2分
8.2AI 综合评分
Google WorkspaceGmail自动化日历管理协作文档工作流自动化
✦ AI Skill Hub 推荐

google_workspace_mcp MCP工具 是 AI Skill Hub 本期精选MCP工具之一。已获得 2.4k 颗 GitHub Star,综合评分 8.2 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。

📚 深度解析
google_workspace_mcp MCP工具 是一款基于 MCP(Model Context Protocol)标准协议的 AI 工具扩展。MCP 协议由 Anthropic 开发并开源,旨在建立 AI 模型与外部工具之间的标准化通信接口,目前已被 Claude Desktop、Claude Code、Cursor 等主流 AI 工具采纳。

通过安装 google_workspace_mcp MCP工具,你的 AI 助手将获得额外的工具调用能力,可以用自然语言直接操控该工具的功能,无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置,永久增强"——配置完成后,每次与 AI 对话时都可以无缝调用这些工具。

在技术实现上,MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信,工具的功能以"工具列表"的形式暴露给 AI 模型,AI 可以按需调用。google_workspace_mcp MCP工具 提供了结构化的工具调用接口,使 AI 模型能够精确地理解和使用每个功能点,显著降低 AI 在工具使用上的错误率。

与传统的 API 集成相比,MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON,即可让 AI 获得全新能力。AI Skill Hub 将 google_workspace_mcp MCP工具 评为 AI 评分 8.2 分,属于同类工具中的优质选择。
📋 工具概览

google_workspace_mcp 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。

GitHub Stars
⭐ 2.4k
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
持续维护,定期更新
开源协议
MIT
AI 综合评分
8.2 分
工具类型
MCP工具
Forks
729
📖 中文文档
以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

google_workspace_mcp 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。

📌 核心特色
  • 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
  • 提供结构化工具调用接口,显著降低 AI 集成复杂度
  • 支持 Claude Desktop 和 Claude Code 无缝接入,开箱即用
  • 可与其他 MCP 工具组合叠加,构建完整 AI 工作站
  • 轻量无侵入设计,不影响现有系统架构
🎯 主要使用场景
  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/taylorwilsdon/google_workspace_mcp

# 方式二:手动配置 claude_desktop_config.json
{
  "mcpServers": {
    "google-workspace-mcp-mcp--": {
      "command": "npx",
      "args": ["-y", "google_workspace_mcp"]
    }
  }
}

# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
📋 安装步骤说明
  1. 确认已安装 Node.js(v18 或以上版本)
  2. 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
  3. 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
  4. 保存配置文件并重启 Claude 客户端
  5. 重启后,在对话中即可使用本工具
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 安装后在 Claude 对话中直接使用
# 示例:
用户: 请帮我用 google_workspace_mcp MCP工具 执行以下任务...
Claude: [自动调用 google_workspace_mcp 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"
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
// claude_desktop_config.json 配置示例
{
  "mcpServers": {
    "google_workspace_mcp_mcp__": {
      "command": "npx",
      "args": ["-y", "google_workspace_mcp"],
      "env": {
        // "API_KEY": "your-api-key-here"
      }
    }
  }
}

// 保存后重启 Claude Desktop 生效
📑 README 深度解析 真实文档 完整度 74/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

<span style="color:#adbcbc">Overview</span>

Workspace MCP is the single most complete MCP server, the only that integrates all major Google Workspace services with AI assistants and all agent platforms. The entire toolset is available for CLI usage supporting both local and remote instances.

<span style="color:#adbcbc">Features</span>

12 services &ensp;—&ensp; Gmail · Drive · Calendar · Docs · Sheets · Slides · Forms · Chat · Apps Script · Tasks · Contacts · Search

📧 Gmail — Complete email management, end-to-end coverage<br> 📁 Drive — File operations with sharing, permissions, Office files, PDFs & images<br> 📅 Calendar — Full event management with advanced features<br> 📝 Docs — Deep, fine-grained editing, formatting & comments<br> 📊 Sheets — Flexible cell management, formatting & conditional rules<br> 🖼️ Slides — Presentation creation, updates & content manipulation<br> 📋 Forms — Creation, publish settings & response management<br> 💬 Chat — Space management, messaging & reactions

</td> <td valign="top" width="50%">

⚡ Apps Script — Cross-application workflow automation<br> <sub>&ensp;Projects · deployments · versions · execution · debugging</sub>

✅ Tasks — Task & list management with hierarchy<br> 👤 Contacts — People API with groups & batch operations<br> 🔍 Custom Search — Programmable Search Engine integration

---

🔐 Authentication & Security<br> <sub>OAuth 2.0 & 2.1 · auto token refresh · multi-user bearer tokens · transport-aware callbacks · CORS proxy</sub>

</td> </tr> </table>

---

2. Launch — OAuth 2.1 requires HTTP transport

uvx workspace-mcp --transport streamable-http --tool-tier core uvx workspace-mcp --transport streamable-http --tool-tier extended uvx workspace-mcp --transport streamable-http --tool-tier complete

Prerequisites

Python 3.10+ · uv/uvx · Google Cloud Project with OAuth 2.0 credentials

If you want the GCS credential store backend, install the optional dependency first:

```bash uv sync --extra gcs

Requires Python 3.10+ and uvx

Execute the full test suite (async fixtures require pytest-asyncio)

uv run pytest ```

  • uv sync --group test installs only the testing stack if you need a slimmer environment.
  • uv run main.py --transport streamable-http launches the server with your checked-out code for manual verification.
  • Ruff is part of the dev group because pre-push hooks call ruff check automatically—run it locally before committing to avoid hook failures.

</details>

OAuth 2.1 requires HTTP transport mode

export MCP_ENABLE_OAUTH21=true uv run main.py --transport streamable-http


If `MCP_ENABLE_OAUTH21` is not set to `true`, the server will use legacy authentication, which is suitable for clients that do not support OAuth 2.1.

<details open>
<summary>🔐 <b>How the FastMCP GoogleProvider handles OAuth</b> <sub><sup>← Advanced OAuth 2.1 details</sup></sub></summary>

FastMCP ships a native `GoogleProvider` that we now rely on directly. It solves the two tricky parts of using Google OAuth with MCP clients:

1.  **Dynamic Client Registration**: Google still doesn't support OAuth 2.1 DCR, but the FastMCP provider exposes the full DCR surface and forwards registrations to Google using your fixed credentials. MCP clients register as usual and the provider hands them your Google client ID and, when configured, client secret under the hood.

2.  **CORS & Browser Compatibility**: The provider includes an OAuth proxy that serves all discovery, authorization, and token endpoints with proper CORS headers. We no longer maintain custom `/oauth2/*` routes—the provider handles the upstream exchanges securely and advertises the correct metadata to clients.

The result is a leaner server that still enables any OAuth 2.1 compliant client (including browser-based ones) to authenticate through Google without bespoke code.

**Restricting DCR client redirect URIs:**

By default, any client going through Dynamic Client Registration can declare any `redirect_uri`. For publicly-exposed deployments, this is a phishing vector — an attacker can register a client with a `redirect_uri` they control and harvest authorization codes from tricked users. Set `WORKSPACE_MCP_ALLOWED_CLIENT_REDIRECT_URIS` to a comma-separated allowlist of permitted URIs:
bash

Stateless mode requires OAuth 2.1 to be enabled

export MCP_ENABLE_OAUTH21=true export WORKSPACE_MCP_STATELESS_MODE=true uv run main.py --transport streamable-http ```

Key Features: - No file system writes: Credentials are never written to disk - No debug logs: File-based logging is completely disabled - Memory-only sessions: All tokens stored in memory via OAuth 2.1 session store - Container-ready: Perfect for Docker, Kubernetes, and serverless deployments - Token per request: Each request must include a valid Bearer token

Requirements: - Must be used with MCP_ENABLE_OAUTH21=true - Incompatible with single-user mode - Clients must handle OAuth flow and send valid tokens with each request

This mode is ideal for: - Cloud deployments where persistent storage is unavailable - Multi-tenant environments requiring strict isolation - Containerized applications with read-only filesystems - Serverless functions and ephemeral compute environments

MCP Inspector: No additional configuration needed with desktop OAuth client.

Claude Code: No additional configuration needed with desktop OAuth client.

External OAuth provider mode requires OAuth 2.1 to be enabled

export MCP_ENABLE_OAUTH21=true export EXTERNAL_OAUTH21_PROVIDER=true uv run main.py --transport streamable-http ```

How It Works: - Protocol-level auth enabled: All MCP requests (including initialize and tools/list) require a valid Bearer token, following the standard OAuth 2.1 flow. Unauthenticated requests receive a 401 with resource metadata pointing to Google's authorization server. - External OAuth flow: Your external system handles the OAuth flow and obtains Google access tokens (ya29.*) - Token validation: Server validates bearer tokens by calling Google's userinfo API - Multi-user support: Each request is authenticated independently based on its bearer token - Resource metadata discovery: The server serves /.well-known/oauth-protected-resource (RFC 9728) advertising Google as the authorization server and the required scopes

Key Features: - No local OAuth flow: Server does not provide /authorize, /token, or /register endpoint

Google Custom Search Setup

<details open> <summary>◆ <b>Custom Search Configuration</b> <sub><sup>← Enable web search capabilities</sup></sub></summary>

1. Create Search Engine

programmablesearchengine.google.com
/controlpanel/create

→ Configure sites or entire web
→ Note your Engine ID (cx)
<sub>Open Control Panel →</sub>

</td> <td width="33%" align="center">

2. Get API Key

developers.google.com
/custom-search/v1/overview

→ Create/select project
→ Enable Custom Search API
→ Create credentials (API Key)
<sub>Get API Key →</sub>

</td> <td width="34%" align="center">

3. Set Variables

export GOOGLE_PSE_API_KEY=\
  "your-api-key"
export GOOGLE_PSE_ENGINE_ID=\
  "your-engine-id"
<sub>Configure in environment</sub>

</td> </tr> <tr> <td colspan="3">

<details open> <summary>≡ <b>Quick Setup Guide</b> <sub><sup>← Step-by-step instructions</sup></sub></summary>

Complete Setup Process:

1. Create Search Engine - Visit the Control Panel - Choose "Search the entire web" or specify sites - Copy the Search Engine ID (looks like: 017643444788157684527:6ivsjbpxpqw)

2. Enable API & Get Key - Visit Google Developers Console - Enable "Custom Search API" in your project - Create credentials → API Key - Restrict key to Custom Search API (recommended)

3. Configure Environment - Add to your shell or .env:

   export GOOGLE_PSE_API_KEY="AIzaSy..."
   export GOOGLE_PSE_ENGINE_ID="01764344478..."
   

Full Documentation →

</details>

</td> </tr> </table>

</details>

Or, if installed globally:

workspace-cli list workspace-cli --url https://custom.server/mcp list

<sub>View all available tools</sub>

</td>
<td width="50%" align="center">

**◆ Call a Tool**
bash uv run workspace-cli call search_gmail_messages \ query="is:unread" max_results=5
<sub>Execute a tool with key=value arguments</sub>

</td>
</tr>
</table>

Set URL for remote endpoints with `--url` or the `WORKSPACE_MCP_URL` environment variable.

<details open>
<summary>≡ <b>Advanced: FastMCP CLI</b> <sub><sup>← inspect, install, discover</sup></sub></summary>

The upstream [FastMCP CLI](https://gofastmcp.com/cli) is also bundled and provides additional commands for schema inspection, client installation, and editor discovery. Note that `fastmcp` uses in-memory token storage, so each invocation may re-trigger the OAuth flow.
bash fastmcp inspect fastmcp_server.py # print tools, resources, prompts fastmcp install claude-code fastmcp_server.py # one-command client setup fastmcp install cursor fastmcp_server.py fastmcp discover # find servers configured in editors ```

See fastmcp --help or the FastMCP CLI docs for the full command reference.

</details>

</details>

2. Advanced / Cross-Platform Installation

If you’re developing, deploying to servers, or using another MCP-capable client, keep reading.

Instant CLI (uvx)

<details open> <summary>⚡ <b>Quick Start with uvx</b> <sub><sup>← No installation required!</sup></sub></summary>

```bash

Local Development Setup

<details open> <summary>🛠️ <b>Developer Workflow</b> <sub><sup>← Install deps, lint, and test</sup></sub></summary>

```bash

Install everything needed for linting, tests, and release tooling

uv sync --group dev

Public deployment — restrict to Claude's hosted OAuth callbacks

export WORKSPACE_MCP_ALLOWED_CLIENT_REDIRECT_URIS="https://claude.ai/api/mcp/auth_callback,https://claude.com/api/mcp/auth_callback"

Quick Start

Set credentials → pick a launch command → connect your client
💡 New to Workspace MCP? Check out the Interactive Quick Start Guide → with step-by-step setup, screenshots, and troubleshooting tips!

</div>

Confidential Client Quick Start

```bash

Quick Start — Connect Claude to Google Workspace

The recommended setup is to run an instance and connect Claude to it via a Connector. Full instructions at workspacemcp.com/quick-start.

---

Configuration

<details open> <summary><b>Google Cloud Setup</b></summary>

1. Create ProjectOpen Console → → Create new project 2. Create OAuth Credentials — APIs & Services → Credentials → Create Credentials → OAuth Client ID - Choose Desktop Application for a public PKCE client (no redirect URIs needed) or Web Application for a confidential client - Download and note your Client ID and, if issued, Client Secret 3. Enable APIs — APIs & Services → Library, then enable each service:

| | | | | |:--|:--|:--|:--| | Calendar | Drive | Gmail | Docs | | Sheets | Slides | Forms | Tasks | | Chat | People | Custom Search | Apps Script |

4. Set Credentials — see Environment Variable Reference above, or:

   export GOOGLE_OAUTH_CLIENT_ID="your-client-id"
   export GOOGLE_OAUTH_CLIENT_SECRET="your-secret"
   
For public OAuth 2.1 PKCE clients, omit GOOGLE_OAUTH_CLIENT_SECRET and set FASTMCP_SERVER_AUTH_GOOGLE_JWT_SIGNING_KEY instead.

<sub>Full OAuth documentation → · Credential setup details →</sub>

</details>

Optional bridge only for local legacy stdio sessions

WORKSPACE_MCP_HTTP_PORT=8001 uv run main.py workspace-cli --url http://127.0.0.1:8001/mcp list

The sidecar is disabled unless `WORKSPACE_MCP_HTTP_PORT` is set. It only exists to bridge local `workspace-cli` calls into a legacy stdio server. Do not use it for normal Claude Code, VS Code, hosted, or multi-user deployments; use streamable HTTP with OAuth 2.1 instead. When enabled, it validates ports in the `1..65535` range, binds to `127.0.0.1`, and logs a warning if the port is already in use while keeping stdio running.

**★ Tool Tiers**
bash uv run main.py --tool-tier core # ● Essential tools only uv run main.py --tool-tier extended # ◐ Core + additional uv run main.py --tool-tier complete # ○ All available tools

**◆ Docker Deployment**
bash docker build -t workspace-mcp . docker run -p 8000:8000 -v $(pwd):/app \ workspace-mcp --transport streamable-http

With tool selection via environment variables

docker run -e TOOL_TIER=core workspace-mcp docker run -e TOOLS="gmail drive calendar" workspace-mcp ```

Available Services: gmaildrivecalendardocssheetsformstaskscontactschatsearch

</details>

</td> </tr> </table>

</details>

📋 Credential Configuration

<details open> <summary>🔑 <b>OAuth Credentials Setup</b> <sub><sup>← Essential for all installations</sup></sub></summary>

🚀 Environment Variables

export GOOGLE_OAUTH_CLIENT_ID=\
  "your-client-id"
export GOOGLE_OAUTH_CLIENT_SECRET=\
  "your-secret"
<sub>Best for production</sub>

</td> <td width="33%" align="center">

📁 File-based ```bash

Edit .env with credentials

``` <sub>Best for development</sub>

</td> </tr> <tr> <td colspan="3">

<details open> <summary>📖 <b>Credential Loading Details</b> <sub><sup>← Understanding priority & best practices</sup></sub></summary>

Loading Priority 1. Environment variables (export VAR=value) 2. .env file in project root (warning - if you run via uvx rather than uv run from the repo directory, you are spawning a standalone process not associated with your clone of the repo and it will not find your .env file without specifying it directly) 3. client_secret.json via GOOGLE_CLIENT_SECRET_PATH 4. Default client_secret.json in project root

Why Environment Variables? - ✅ Docker/K8s ready - Native container support - ✅ Cloud platforms - Heroku, Railway, Vercel - ✅ CI/CD pipelines - GitHub Actions, Jenkins - ✅ No secrets in git - Keep credentials secure - ✅ Easy rotation - Update without code changes

</details>

</td> </tr> </table>

</details>

---

First, set credentials (see Credential Configuration above)

uvx workspace-mcp --tool-tier core # or --tools gmail drive calendar ```

Note: Configure OAuth credentials before running. Supports environment variables, .env file, or client_secret.json.

</details>

Add Claude Code CLI (loopback redirects on ephemeral ports)

export WORKSPACE_MCP_ALLOWED_CLIENT_REDIRECT_URIS="https://claude.ai/api/mcp/auth_callback,https://claude.com/api/mcp/auth_callback,http://localhost:/callback,http://127.0.0.1:/callback" ```

Patterns use FastMCP's matcher: * wildcards any port or path component; *.example.com matches subdomains. Leaving the variable unset preserves the default DCR behaviour (any URI accepted), which is appropriate for local development but unsafe for public deployments.

</details>

🎯 aiskill88 AI 点评 A 级 2026-05-21

星标数量可观的成熟MCP工具,覆盖Google Workspace全景应用,API设计完善,代码维护活跃,是办公自动化领域的优质选择。

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
  • Python 依赖冲突:建议用 venv / uv 隔离环境
部署方案
  • Docker:google_workspace_mcp 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 本地部署:CPU 8GB 起,GPU 推荐 16GB+ 显存
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
  • 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
  • 提供结构化工具调用接口,显著降低 AI 集成复杂度
  • 支持 Claude Desktop 和 Claude Code 无缝接入,开箱即用
  • 可与其他 MCP 工具组合叠加,构建完整 AI 工作站
  • 轻量无侵入设计,不影响现有系统架构
👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • 本地部署优先选 GGUF 量化模型,节省显存并保持响应速度
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
  • 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
👥 适合人群
Claude Desktop / Claude Code 用户AI 工具开发者需要扩展 AI 能力的专业人士自动化工程师
🎯 使用场景
  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站
⚖️ 优点与不足
✅ 优点
  • +MIT 协议,可免费商用
  • +标准化 MCP 协议,生态互联性强
  • +与 Claude 官方生态无缝对接
  • +即插即用,配置简单快捷
⚠️ 不足
  • 依赖 Claude 客户端,非 Claude 用户无法使用
  • MCP 协议仍在持续演进,接口可能变更
  • 需要一定的配置步骤
⚠️ 使用须知

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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。

📄 License 说明

✅ 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。

❓ 常见问题 FAQ
需配置Google Cloud项目和相应API权限(Gmail、Calendar、Docs等)。
💡 AI Skill Hub 点评

经综合评估,google_workspace_mcp MCP工具 在MCP工具赛道中表现稳健,质量优秀。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。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ MIT 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 google_workspace_mcp MCP工具
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🌐 原始信息
原始名称 google_workspace_mcp
原始描述 开源MCP工具:Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms, Tasks, Search。⭐2.4k · Python
Topics Google WorkspaceGmail自动化日历管理协作文档工作流自动化
GitHub https://github.com/taylorwilsdon/google_workspace_mcp
License MIT
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/taylorwilsdon/google_workspace_mcp 🌐 官方网站  https://workspacemcp.com

收录时间:2026-05-14 · 更新时间:2026-05-16 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。