
Genudo
io.github.genudo-ai由 GitHub 账号 genudo-ai 发布v2.5.0更新于 Oct 26, 2026
Build & manage Genudo AI agents for any messaging channel — pipelines, integrations, knowledge.
概览
本地桥接工具,把 MCP 客户端连接到 Genudo 工作区,让助手构建、运行和审计面向消息渠道的 AI 智能体。
- 可以做什么
- 读取账户、消息量和 AI 性能分析数据
- 列出并搜索流水线、阶段、动作、变量、联系人、商机、消息和知识表
- 创建流水线、阶段、变量、Webhook 动作、跟进序列和知识表,并提供分步引导式构建
- 更新线上流水线、阶段、动作、变量、批量商机和跟进配置
- 按稳定 id 删除知识行
- 将 Shopify 商品目录导出为 CSV 或 Excel 知识库
- 适用场景
- 适合在 Genudo 上运营消息或序列类渠道智能体时使用:想查看效果数据、新建或调整流水线,或在聊天客户端里管理知识库而不必打开 Genudo 后台。
- 运行要求
- 通过 npx 在本地运行,需要 Node.js 18 或更高版本,也可从源码运行。需要 Genudo 账户,以及具备 mcp:use 权限范围的令牌,通过 GENUDO_TOKEN 环境变量提供。自建实例可设置 GENUDO_BASE_URL;需要能访问 Genudo 端点。
安装
在 SourceWeft 中
- 打开 控制台中的 Genudo,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
Genudo MCP Client
[npm version] [License: MIT] [Node.js Version]
Genudo is the platform to build AI agents for any communication or sequence-based channel — pipeline-aware agents with integration capabilities and company-knowledge access, all managed from one platform, one inbox, and one analytics dashboard. This lightweight bridge connects Claude and other MCP clients to your Genudo workspace so you can build, run, and improve those agents directly from chat.
📖 Which install is right for you? See CONNECT.md — every way to connect Genudo to Claude (plugin, connector, desktop extension, other MCP clients, remote OAuth), weighted by value with copy-paste install commands.
Overview
Genudo MCP Client bridges the communication gap between Claude Code's stdio-based MCP implementation and Genudo's Streamable HTTP MCP server. This allows you to leverage Genudo's powerful business automation tools directly within your Claude Code conversations.
Features
Access Genudo's business automation tools directly from Claude Code. The server exposes 29 tools across five areas:
- 📊 Analytics — account summary, messaging volume stats, AI performance & cost (
get_account_summary,get_messaging_stats,get_ai_performance) - 🔎 Discover — list pipelines, stages, actions, variables, contacts, opportunities, messages, and knowledge tables; search knowledge; read follow-up configs; valid build options (
list_pipelines,list_pipeline_stages,list_actions,list_variables,list_contacts,list_opportunities,list_messages,list_knowledge_tables,search_knowledge_table,get_stage_followup,get_pipeline_options) - 🚀 Build — a guided step-by-step builder plus direct creation of pipelines, stages, variables, webhook actions, follow-up sequences, and knowledge tables (
start_pipeline_journey,create_pipeline,create_stage,create_variable,create_action,create_followup,create_knowledge_table,upsert_knowledge_points) - 🔧 Update — tune live pipelines, stages, actions, variables, opportunities (bulk), and follow-ups (
update_pipeline,update_stage,update_action,update_variable,update_opportunities,update_followup) - 🗑️ Delete — remove knowledge rows by stable id (
delete_knowledge_points) - 🛍️ Shopify Knowledge Exporter — dynamically extract products via GraphQL, strip HTML, normalize Arabic & English text, and generate clean CSV with UTF-8 BOM and formatted Excel knowledge bases with auto-translated Arabic titles (
shopify_export_catalog)
Three workflows to start with
- Audit my AI business operations — Claude pulls your account, messaging, and AI-cost stats and surfaces the top fixes.
- Analyze my agent performance — spot expensive agents, low-completion stages, and actions that aren't triggering.
- Create or improve a pipeline — e.g. "Create a Messenger sales agent for a summer camp; collect name, phone, branch, child age; escalate on special-needs questions."
Guided instruction editing (built in)
The connector doesn't just expose tools — it teaches the agent how to write good agent instructions, entirely client-side (no extra setup):
- Guide tools —
get_instruction_guides(authoring rules + persona/global + stage templates + a token-aware QA checklist) andget_editing_playbook(a safe load → edit → diff → confirm → push workflow). The agent pulls these on demand. - Prompts (slash-commands in any MCP client) —
edit_instructions,build_pipeline,audit_pipeline. - Safety — before changing a live agent, the agent reads current text from
list_pipelines/list_pipeline_stages, shows you a before/after diff with expected impact, and pushes withupdate_pipeline/update_stageonly after you confirm.
Quick Start
One command — no clone, no manual config:
Replace YOUR_TOKEN with your Genudo token (API Keys & Tokens → Create token with the mcp:use scope — see Get Your Token). Restart Claude Code and start using Genudo tools!
Prerequisites
- Node.js 18.0.0 or higher
- A Genudo account with API access
- Your Genudo token
Installation
Recommended: one command
npx fetches and runs the published package — no clone, no local path. Get your token from API Keys & Tokens (see below), then restart Claude Code.
Alternative: from source
For local development or contributions:
Then configure Claude Code manually (see Configuration section below).
Configuration
Step 1: Get Your Token
- Log in to your Genudo account
- In the sidebar, under Developer, open API Keys & Tokens
- Click Create token
- Name it (e.g.
claude-mcp) - Under Scopes, scroll the list and check
mcp:use(Access MCP server — Streamable HTTP + JSON-RPC tool calls) - Pick an Expiry (30 days, 90 days, 1 year, or No Expiry)
- Click Create token and copy the token — it's shown only once; Genudo keeps only a hashed copy
Step 2: Connect your MCP client
Recommended — Claude Code, one command:
Google Antigravity & Gemini CLI (agy): Install the official plugin from antigravity/genudo/:
Or check it in per-project at .agents/plugins/genudo/. See antigravity/genudo/README.md for full instructions.
Other clients (Codex, Cursor, Windsurf, Claude Desktop): same package, add this block to the client's MCP config:
Running from source instead (contributors)
If you cloned the repo rather than using the published package, point at your local index.js:
Replace the path with where you cloned the repo.
Step 3: Restart Claude Code
Exit your current Claude Code session and start a new one:
Usage
Once configured, Claude Code will automatically have access to your Genudo tools. You can use them in your conversations:
Example:
Testing the Connection
You can test the bridge manually:
Troubleshooting
"GENUDO_TOKEN environment variable is required"
- Make sure you've set the token in your Claude Code config
- Verify the token is valid in your Genudo account
"MCP handshake failed" / no response
- Check that
GENUDO_BASE_URLis correct (defaults tohttps://api.genudo.ai) — the bridge POSTs to<base>/mcp - Verify your server is running and accessible
- Check firewall/network settings
"HTTP 401: Unauthorized"
- Your token is invalid or expired
- Generate a new token in your Genudo account settings
Connection Issues with Self-Signed Certificates
- The bridge is configured to accept self-signed certificates for local development
- For production, use proper SSL certificates
Environment Variables
Development
Project Structure
How It Works
- Bridge performs the MCP handshake against
<base>/mcp(Streamable HTTP) - Reads JSON-RPC requests from stdin (from Claude Code)
- Forwards each one as an HTTP POST to that same endpoint
- Returns responses via stdout back to Claude Code
Since v2.5.0 there is no SSE step: Streamable HTTP is a single endpoint that takes
every message as a POST. If you don't need the bridge's local guide tools, prompts or
response slimming, you can skip it entirely and point your client at the server:
claude mcp add --transport http genudo https://api.genudo.ai/mcp
Security Notes
- Never commit your token to version control
- Store tokens securely (environment variables, secrets manager)
- Rotate tokens regularly
- Use HTTPS in production
- Limit token scopes to minimum required access
Privacy Policy
This client is a local bridge. It does not collect, store, or transmit your data anywhere except your configured Genudo endpoint.
- Data handled: your
GENUDO_TOKENand the JSON-RPC requests your MCP client makes are sent, over HTTPS, only to your Genudo server (https://api.genudo.aiby default) in theAuthorization: Bearerheader. - Local storage: none. The bridge keeps no logs, no cache, and no telemetry; it holds nothing on disk. Your token stays in your MCP client's own config.
- Third parties: no data is sent to any party other than Genudo.
- Retention & account data: data you access or create through Genudo is governed by Genudo's policies.
Full policies: Privacy Policy · Terms of Service. Questions: [email protected].
License
MIT
Support
Need help? We're here for you:
Contributing
We welcome contributions! Please see CONTRIBUTING.md for details on how to get started.
Quick overview:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Changelog
See the GitHub Releases page for changes and version history.
Related Projects
- Genudo - The main Genudo automation platform
- Model Context Protocol - Learn more about MCP
- Claude Code - The Claude AI coding assistant
来源:README.md,提交 522db23
工具
0版本历史
1- v2.5.0最新Sep 16, 2026
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