A lightweight bridge that connects Claude Code to Genudo's Model Context Protocol (MCP) server, enabling AI-powered workflow automation directly from your Claude Code interface.
claude mcp add genudo-mcp -- npx -y genudo-mcp-client{
"mcpServers": {
"genudo-mcp": {
"command": "npx",
"args": ["-y", "genudo-mcp-client"],
"env": {
"GENUDO_TOKEN": "<genudo_token>"
}
}
}
}GENUDO_TOKENMCP Servers overview
# Genudo MCP Client
[](https://www.npmjs.com/package/genudo-mcp-client)
[](https://opensource.org/licenses/MIT)
[](https://nodejs.org)
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](https://claude.com) 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](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 HTTP/SSE-based 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`)
### Three workflows to start with
1. **Audit my AI business operations** — Claude pulls your account, messaging, and AI-cost stats and surfaces the top fixes.
2. **Analyze my agent performance** — spot expensive agents, low-completion stages, and actions that aren't triggering.
3. **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) and `get_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 with `update_pipeline` / `update_stage` only after you confirm.
## Quick Start
One command — no clone, no manual config:
```bash
claude mcp add --env GENUDO_TOKEN=YOUR_TOKEN --transport stdio genudo -- npx -y genudo-mcp-client
```
Replace `YOUR_TOKEN` with your Genudo token (**API Keys & Tokens** → **Create token** with the `mcp:use` scope — see [Get Your Token](#step-1-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
```bash
claude mcp add --env GENUDO_TOKEN=YOUR_TOKEN --transport stdio genudo -- npx -y genudo-mcp-client
```
`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:
```bash
git clone https://github.com/genudo-ai/genudo_mcp.git
cd genudo_mcp
npm install
```
Then configure Claude Code manually (see Configuration section below).
## Configuration
### Step 1: Get Your Token
1. Log in to your [Genudo account](https://app.genudo.ai)
2. In the sidebar, under **Developer**, open **API Keys & Tokens**
3. Click **Create token**
4. Name it (e.g. `claude-mcp`)
5. Under **Scopes**, scroll the list and check **`mcp:use`** (Access MCP server — SSE + JSON-RPC tool calls)
6. Pick an **Expiry** (30 days, 90 days, 1 year, or No Expiry)
7. 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:**
```bash
claude mcp add --env GENUDO_TOKEN=YOUR_TOKEN --transport stdio genudo -- npx -y genudo-mcp-client
```
**Other clients (Codex, Cursor, Windsurf, Claude Desktop):** same package, add this block to the client's MCP config:
```json
{
"mcpServers": {
"genudo": {
"command": "npx",
"args": ["-y", "genudo-mcp-client"],
"env": {
"GENUDO_TOKEN": "your_token_here"
}
}
}
}
```
<details>
<summary>Running from source instead (contributors)</summary>
If you cloned the repo rather than using the published package, point at your local `index.js`:
```json
{
"mcpServers": {
"genudo": {
"command": "node",
"args": ["/absolute/path/to/genudo_mcp/index.js"],
"env": { "GENUDO_TOKEN": "your_token_here" }
}
}
}
```
Replace the path with where you cloned the repo.
</details>
### Step 3: Restart Claude Code
Exit your current Claude Code session and start a new one:
```bash
# Exit current session
exit
# Start new session
claude
```
## Usage
Once configured, Claude Code will automatically have access to your Genudo tools. You can use them in your conversations:
**Example:**
```
User: "Show me my account summary"
Claude: [Uses get_account_summary tool to fetch your data]
User: "Create a new sales pipeline using GPT-4"
Claude: [Uses get_pipeline_options and create_pipeline tools]
```
## Testing the Connection
You can test the bridge manually:
```bash
# Set your token
export GENUDO_TOKEN="your_token_here"
# Run the bridge
node index.js
# Send a test message (paste this and press Enter):
{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}
# You should receive a response with server info
```
## 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
### "Timeout waiting for endpoint from SSE"
- Check that `GENUDO_BASE_URL` is correct (defaults to `https://api.genudo.ai`)
- 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
| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| `GENUDO_TOKEN` | **Yes** | - | Your Genudo token from account settings |
| `GENUDO_BASE_URL` | No | `https://api.genudo.ai` | Base URL for self-hosted Genudo instances |
| `GENUDO_ALLOW_INSECURE_SSL` | No | `false` | Allow self-signed SSL certificates (local development only) |
| `GENUDO_REQUEST_TIMEOUT` | No | `8000` | Per-attempt request timeout in ms before retrying |
| `GENUDO_REQUEST_RETRIES` | No | `4` | Number of attempts for a request before giving up |
| `GENUDO_WORKDIR` | No | current directory | Root the agent writes local pipeline mirrors, version snapshots and cached guides into |
## Development
### Project Structure
```
genudo-mcp-client/
├── index.js # Main bridge script
├── package.json # Node.js project config
├── .env.example # Environment variables template
└── README.md # This file
```
### How It Works
1. Bridge connects to SSE endpoint to get the message POST URL
2. Reads JSON-RPC requests from stdin (from Claude Code)
3. Forwards requests as HTTP POST to the Genudo server
4. Returns responses via stdout back to Claude Code
```
Claude Code → (stdin) → Bridge → (HTTPS) → Genudo MCP Server
↑ ↓
└─────── (stdout) ←────────────┘
```
## 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_TOKEN` and the JSON-RPC requests your MCP
client makes are sent, over HTTPS, only to your Genudo server
(`https://api.genudo.ai` by default) in the `Authorization: Bearer` header.
- **Local storage:** none. The bridge keeps no logs, no cache, and no telemetry;
it holds nothing on disk. Your token stays in your MCP cliWhat people ask about genudo_mcp
What is genudo-ai/genudo_mcp?
+
genudo-ai/genudo_mcp is mcp servers for the Claude AI ecosystem. A lightweight bridge that connects Claude Code to Genudo's Model Context Protocol (MCP) server, enabling AI-powered workflow automation directly from your Claude Code interface. It has 0 GitHub stars and was last updated today.
How do I install genudo_mcp?
+
You can install genudo_mcp by cloning the repository (https://github.com/genudo-ai/genudo_mcp) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is genudo-ai/genudo_mcp safe to use?
+
genudo-ai/genudo_mcp has not been audited yet by our security agent. Review the original repository on GitHub before using it in production.
Who maintains genudo-ai/genudo_mcp?
+
genudo-ai/genudo_mcp is maintained by genudo-ai. The last recorded GitHub activity is from today, with 0 open issues.
Are there alternatives to genudo_mcp?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
Deploy genudo_mcp to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/genudo-ai-genudo-mcp)<a href="https://claudewave.com/repo/genudo-ai-genudo-mcp"><img src="https://claudewave.com/api/badge/genudo-ai-genudo-mcp" alt="Featured on ClaudeWave: genudo-ai/genudo_mcp" width="320" height="64" /></a>More MCP Servers
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
An open-source AI agent that brings the power of Gemini directly into your terminal.
The fastest path to AI-powered full stack observability, even for lean teams.
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!
Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface