@scalogicai/n8n-nodes-mcp-enhanced

Enhanced MCP Client with Additional Features

This is an enhanced version of the n8n MCP client with additional features for prompt parameters and multi-tenant support, designed for users who need these specific capabilities.

🙏 Attribution

This enhanced node is built upon the excellent foundation created by Jd Fiscus (@nerding-io):

• Original Project: n8n-nodes-mcp
• npm Package: n8n-nodes-mcp
• Creator: Jd Fiscus (jd@nerding.io)

We're grateful for the original work that made this enhanced version possible. All core MCP protocol implementation and transport handling comes from the original project.

🔧 What's Different?

This version adds two specific features while maintaining full compatibility with the original:

Additional Features:

• Prompt Parameters: Pass JSON parameters to prompts (original accepts prompt name only)
• Tenant ID Support: Optional field for multi-tenant MCP servers
• Enhanced Validation: Additional JSON parameter validation and error handling

What's Identical:

• Resource handling: listResources, readResource, listResourceTemplates work exactly the same
• Tool execution: Same functionality for executing MCP tools
• Transport types: Same support for STDIO, SSE, and HTTP Streamable
• Core MCP protocol: Full compatibility with MCP specification

🎯 When to Use This Version

Choose this enhanced version if:

• Your MCP server accepts parameterized prompts
• You work with multi-tenant MCP implementations
• You need the specific additional features listed above

Otherwise, the original version works perfectly for standard MCP operations!

🚀 Additional Features in Detail

1. Prompt Parameters

Original Version:

- Prompt Name: "write_email"
- Result: Gets generic email template

Enhanced Version:

- Prompt Name: "write_email"
- Prompt Parameters: {"tone": "formal", "recipient": "manager", "topic": "project update"}
- Result: Gets customized email with your specific requirements

2. Tenant ID Support

Enhanced Version:

- Prompt Name: "format_document"
- Prompt Parameters: {"style": "academic"}
- Tenant ID: "team-marketing"
- Result: Gets team-specific document formatting and templates

📝 Parameter Formatting Guide

JSON Parameter Examples:

Simple text customization:

{
  "language": "Spanish", 
  "style": "casual"
}

Content parameters:

{
  "topic": "renewable energy",
  "audience": "general public",
  "length": "short",
  "include_examples": true
}

User preferences:

{
  "user": {
    "name": "Alex",
    "skill_level": "beginner"
  },
  "output_format": "markdown",
  "include_links": false
}

Complex nested parameters:

{
  "content": {
    "type": "tutorial",
    "sections": ["intro", "examples", "conclusion"]
  },
  "formatting": {
    "style": "academic",
    "citations": true
  },
  "output": {
    "format": "pdf",
    "language": "English"
  }
}

Common Parameter Types:

• Text: "language": "French"
• Numbers: "word_count": 500
• Booleans: "include_summary": true
• Arrays: "topics": ["climate", "technology", "health"]
• Objects: "format": {"type": "list", "numbered": true}

🌟 Real-World Examples

Example 1: Content Creation

{
  "prompt_name": "write_article",
  "parameters": {
    "topic": "sustainable living",
    "tone": "informative",
    "word_count": 800,
    "target_audience": "beginners",
    "include_tips": true
  }
}

Example 2: Data Processing

{
  "prompt_name": "analyze_data",
  "parameters": {
    "data_type": "sales_figures",
    "time_period": "last_quarter",
    "include_charts": true,
    "focus_areas": ["trends", "anomalies"]
  }
}

Example 3: Personal Workflows

{
  "prompt_name": "organize_notes",
  "parameters": {
    "category": "meeting_notes",
    "priority_level": "high",
    "tags": ["project_alpha", "urgent"],
    "format": "structured_list"
  }
}

Example 4: Team Environments

{
  "prompt_name": "create_report",
  "parameters": {
    "department": "engineering",
    "report_type": "weekly_standup",
    "include_metrics": true
  },
  "tenant_id": "team-backend"
}

🔄 Migration from Original Version

This version is a drop-in replacement for the original:

1. Existing workflows continue to work unchanged
2. New features are optional - only activate when you use them
3. Same operations available: Execute Tool, List Tools, Get Prompt, etc.
4. Same credential types: STDIO, SSE, HTTP Streamable

To use the new features, simply:

• Add parameters to your "Get Prompt" operations when needed
• Include tenant ID if your MCP server supports multi-tenancy
• Leave these fields empty for standard MCP operations

🛠️ Troubleshooting Enhanced Features

JSON Parameter Issues

Invalid JSON Error:

❌ Error: Invalid JSON in prompt parameters
✅ Fix: Ensure proper JSON formatting with quotes around strings

Example fixes:

❌ {name: "John", active: true}
✅ {"name": "John", "active": true}

❌ {"list": [item1, item2]}
✅ {"list": ["item1", "item2"]}

Empty Parameters

If your prompt doesn't need parameters, use:

{}

Or leave the field empty - both work fine.

Tenant ID Usage

• Optional field - only use if your MCP server supports tenants
• Leave empty for single-tenant or standard MCP servers
• No impact on servers that don't use tenant IDs

n8n is a fair-code licensed workflow automation platform.

Installation
Credentials
Environment Variables
Operations
Using as a Tool
Compatibility
Resources

Getting Started

Official Quickstart Video:

Community Videos

Shoutout to all the creators of the following n8n community videos that are great resources for learning how to use this node:

• Is MCP the Future of N8N AI Agents? (Fully Tested!)
• Connect N8N AI Agents to EVERYTHING using MCP?
• Build an AI Agent That Can Use Any Tool (MCP in n8n Tutorial)
• The NEW N8N MCP is an Absolute Game-Changer (Brave Search MCP)
• MCP & n8n Automation: The Ultimate Guide for MCP AI Agents (2025)
• REVOLUÇÃO na criação de AGENTES no N8N com o MCP Server!!! (Portuguese)

If you have a great video that you'd like to share, please let me know and I'll add it to the list!

Interested a deeper dive into MCP?

Check out my YouTube Series MCP Explained for more information about the Model Context Protocol.

Security Assessment

Installation

Follow the installation guide in the n8n community nodes documentation.

Also pay attention to Environment Variables for using tools in AI Agents. It's mandatory to set the N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE environment variable to true if you want to use the MCP Client node as a tool in AI Agents.

Credentials

The MCP Client Enhanced node supports three types of credentials to connect to an MCP server:

Command-line Based Transport (STDIO)

• Command: The command to start the MCP server
• Arguments: Optional arguments to pass to the server command
• Environment Variables: Variables to pass to the server in NAME=VALUE format

HTTP Streamable Transport (Recommended)

• HTTP Streamable URL: The HTTP endpoint that supports streaming responses (e.g., http://localhost:3001/stream)
• Additional Headers: Optional headers to send with requests (format: name:value, one per line)

HTTP Streamable is the recommended and modern method for all new integrations, providing better efficiency and flexibility compared to SSE.

Example: Using a Local MCP Server with HTTP Streamable

This example shows how to connect to a locally running MCP server using HTTP Streamable:

1. Start a local MCP server that supports HTTP Streamable:

   npx @modelcontextprotocol/server-example-streamable
   

2. Configure MCP Client credentials:

   • In the node settings, select Connection Type: HTTP Streamable
   • Create new credentials of type MCP Client Enhanced (HTTP Streamable) API
   • Set HTTP Streamable URL: http://localhost:3001/stream
   • Add any required headers for authentication

3. Create a workflow using the MCP Client Enhanced node:

   • Add an MCP Client Enhanced node
   • Set the Connection Type to HTTP Streamable
   • Select your HTTP Streamable credentials
   • Execute the workflow to see the results

Server-Sent Events (SSE) Transport (Deprecated, still available for legacy use)

• SSE URL: The URL of the SSE endpoint (default: http://localhost:3001/sse)
• Messages Post Endpoint: Optional custom endpoint for posting messages if different from the SSE URL
• Additional Headers: Optional headers to send with requests (format: name:value, one per line)

Deprecated: SSE is deprecated and will not receive further updates, but remains available for legacy compatibility. For new projects, use HTTP Streamable.

Example: Using a Local MCP Server with SSE (legacy)

This example shows how to connect to a locally running MCP server using Server-Sent Events (SSE):

1. Start a local MCP server that supports SSE:

   npx @modelcontextprotocol/server-example-sse
   

2. Configure MCP Client credentials:

   • In the node settings, select Connection Type: Server-Sent Events (SSE)
   • Create new credentials of type MCP Client Enhanced (SSE) API
   • Set SSE URL: http://localhost:3001/sse
   • Add any required headers for authentication

3. Create a workflow using the MCP Client Enhanced node:

   • Add an MCP Client Enhanced node
   • Set the Connection Type to Server-Sent Events (SSE)
   • Select your SSE credentials
   • Execute the workflow to see the results

Note: For new projects, HTTP Streamable is strongly recommended.

Environment Variables

The MCP Client Enhanced node supports passing environment variables to MCP servers using the command-line based transport in two ways:

1. Using the Credentials UI

You can add environment variables directly in the credentials configuration:

This method is useful for individual setups and testing. The values are stored securely as credentials in n8n.

2. Using Docker Environment Variables

For Docker deployments, you can pass environment variables directly to your MCP servers by prefixing them with MCP_:

version: '3'

services:
  n8n:
    image: n8nio/n8n
    environment:
      - MCP_BRAVE_API_KEY=your-api-key-here
      - MCP_OPENAI_API_KEY=your-openai-key-here
      - MCP_CUSTOM_SETTING=some-value
    # other configuration...

These environment variables will be automatically passed to your MCP servers when they are executed.

Example: Using Brave Search MCP Server

This example shows how to set up and use the Brave Search MCP server:

1. Install the Brave Search MCP server:

   npm install -g @modelcontextprotocol/server-brave-search
   

2. Configure MCP Client credentials:

   • Command: npx
   • Arguments: -y @modelcontextprotocol/server-brave-search
   • Environment Variables: BRAVE_API_KEY=your-api-key Add a variables (space comma or newline separated)

3. Create a workflow that uses the MCP Client Enhanced node:

   • Add an MCP Client Enhanced node
   • Select the "List Tools" operation to see available search tools
   • Add another MCP Client Enhanced node
   • Select the "Execute Tool" operation
   • Choose the "brave_search" tool
   • Set Parameters to: {"query": "latest AI news"}

The node will execute the search and return the results in the output.

Example: Multi-Server Setup with AI Agent

This example demonstrates how to set up multiple MCP servers in a production environment and use them with an AI agent:

1. Configure your docker-compose.yml file:

version: '3'

services:
  n8n:
    image: n8nio/n8n
    environment:
      # MCP server environment variables
      - MCP_BRAVE_API_KEY=your-brave-api-key
      - MCP_OPENAI_API_KEY=your-openai-key
      - MCP_SERPER_API_KEY=your-serper-key
      - MCP_WEATHER_API_KEY=your-weather-api-key

      # Enable community nodes as tools
      - N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true
    ports:
      - "5678:5678"
    volumes:
      - ~/.n8n:/home/node/.n8n

2. Create multiple MCP Client Enhanced credentials in n8n:

   Brave Search Credentials:

   • Command: npx
   • Arguments: -y @modelcontextprotocol/server-brave-search

   OpenAI Tools Credentials:

   • Command: npx
   • Arguments: -y @modelcontextprotocol/server-openai

   Web Search Credentials:

   • Command: npx
   • Arguments: -y @modelcontextprotocol/server-serper

   Weather API Credentials:

   • Command: npx
   • Arguments: -y @modelcontextprotocol/server-weather

3. Create an AI Agent workflow:

   • Add an AI Agent node
   • Enable MCP Client Enhanced as a tool
   • Configure different MCP Client Enhanced nodes with different credentials
   • Create a prompt that uses multiple data sources

Example AI Agent prompt:

I need you to help me plan a trip. First, search for popular destinations in {destination_country}.
Then, check the current weather in the top 3 cities.
Finally, find some recent news about travel restrictions for these places.

With this setup, the AI agent can use multiple MCP tools across different servers, all using environment variables configured in your Docker deployment.

Operations

The MCP Client Enhanced node supports the following operations:

• Execute Tool - Execute a specific tool with parameters
• Get Prompt - Get a specific prompt template (with enhanced parameter support)
• List Prompts - Get a list of available prompts
• List Resources - Get a list of available resources from the MCP server
• List Tools - Get a list of available tools
• Read Resource - Read a specific resource by URI

Example: List Tools Operation

The List Tools operation returns all available tools from the MCP server, including their names, descriptions, and parameter schemas.

Example: Execute Tool Operation

The Execute Tool operation allows you to execute a specific tool with parameters. Make sure to select the tool you want to execute from the dropdown menu.

Example: Enhanced Get Prompt Operation

The enhanced Get Prompt operation includes additional fields:

• Prompt Name: Name of the prompt template (same as original)
• Prompt Parameters: JSON parameters to customize the prompt (new feature)
• Tenant ID: Optional tenant identifier for multi-tenant servers (new feature)

Example usage:

Prompt Name: "summarize_text"
Prompt Parameters: {"length": "short", "style": "bullet_points", "language": "English"}
Tenant ID: "team-content" (optional)

Using as a Tool

This node can be used as a tool in n8n AI Agents. To enable community nodes as tools, you need to set the N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE environment variable to true.

Setting the Environment Variable

If you're using a bash/zsh shell:

export N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true
n8n start

If you're using Docker:
Add to your docker-compose.yml file:

environment:
  - N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true

If you're using the desktop app:
Create a .env file in the n8n directory:

N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true

If you want to set it permanently on Mac/Linux:
Add to your ~/.zshrc or ~/.bash_profile:

export N8N_COMMUNITY_PACKAGES_ALLOW_TOOL_USAGE=true

Example of an AI Agent workflow results:

After setting this environment variable and restarting n8n, your MCP Client Enhanced node will be available as a tool in AI Agent nodes.

Compatibility

• Requires n8n version 1.0.0 or later
• Compatible with MCP Protocol version 1.0.0 or later
• Supports both STDIO and SSE transports for connecting to MCP servers
• SSE transport requires a server that implements the MCP Server-Sent Events specification
• Fully backward compatible with workflows using the original MCP client

Support & Questions

For questions about the enhanced features or issues specific to this version:

• Email: contact@scalogic.ai
• Issues: GitHub Issues

For questions about core MCP functionality, please refer to the original project.

Resources

• n8n community nodes documentation
• Model Context Protocol Documentation
• MCP TypeScript SDK
• MCP Transports Overview
• Using SSE in MCP

License

MIT License - see LICENSE file for details

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Built with ❤️ by ScalogicAI | Foundation by Jd Fiscus (@nerding-io)