n8n-mcp-lite

Token-optimized MCP Server for n8n workflows. 80%+ token reduction compared to the standard n8n MCP server, plus Focus Mode for working on specific areas of large workflows.

The Problem

The standard n8n MCP server returns massive JSON payloads:

activeVersion duplicates ALL nodes and connections (2x the data)
shared includes full user profiles with personalization data
Positions ([x, y]) for every node
Empty defaults (options: {}, staticData: null, etc.)
Verbose connection format with redundant fields
A 16-node workflow = 134,918 tokens (!)

Even with the simplified format, large workflows with AI Agents (huge system prompts), Code nodes (long scripts), and multiple Switch branches still consume too many tokens to work efficiently.

The Solution

n8n-mcp-lite solves both problems:

Simplified Format — Transforms n8n's complex JSON into a compact representation (80%+ savings)
Focus Mode — Work on specific areas of large workflows without loading everything

| Feature | n8n-mcp (standard) | n8n-mcp-lite | |---------|---------------------|--------------| | 5-node workflow | ~4,000 tokens | ~500 tokens | | 78-node workflow | ~600,000+ tokens | ~16,500 tokens | | 78-node scan (no params) | N/A | ~5,100 tokens | | Focus on 2 nodes from 38 | ~135,000 tokens | ~2,600 tokens | | Node creation | Full JSON with positions | {name, type, params} | | Connections | Nested objects | {from, to} | | Type names | n8n-nodes-base.httpRequest | httpRequest |

Quick Start

1. Get your n8n API Key

Go to your n8n instance → Settings → API → Create API Key

2. Configure in your AI client

Choose your client below and add the configuration.

Claude Desktop

Edit %APPDATA%\Claude\claude_desktop_config.json (Windows) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "n8n-lite": {
      "command": "npx",
      "args": ["-y", "n8n-mcp-lite"],
      "env": {
        "N8N_HOST": "https://your-n8n.example.com",
        "N8N_API_KEY": "your-api-key-here"
      }
    }
  }
}

Claude Code

Add to your project's .claude/settings.json or global ~/.claude/settings.json:

{
  "mcpServers": {
    "n8n-lite": {
      "command": "npx",
      "args": ["-y", "n8n-mcp-lite"],
      "env": {
        "N8N_HOST": "https://your-n8n.example.com",
        "N8N_API_KEY": "your-api-key-here"
      }
    }
  }
}

Cursor / Cline / Other MCP Clients

{
  "mcpServers": {
    "n8n-lite": {
      "command": "npx",
      "args": ["-y", "n8n-mcp-lite"],
      "env": {
        "N8N_HOST": "https://your-n8n.example.com",
        "N8N_API_KEY": "your-api-key-here"
      }
    }
  }
}

Local Development (from source)

If running from a cloned repo instead of npm:

{
  "mcpServers": {
    "n8n-lite": {
      "command": "node",
      "args": ["C:/path/to/n8n-mcp-lite/dist/index.js"],
      "env": {
        "N8N_HOST": "https://your-n8n.example.com",
        "N8N_API_KEY": "your-api-key-here"
      }
    }
  }
}

3. Add the System Prompt (recommended)

For best results, add the contents of SYSTEM_PROMPT.md to your AI client's system prompt or custom instructions. This teaches the AI how to use Focus Mode effectively and follow token-efficient patterns.

Environment Variables

| Variable | Required | Description | |----------|----------|-------------| | N8N_HOST | Yes | n8n instance URL (e.g., https://your-n8n.example.com) | | N8N_API_KEY | Yes | n8n API key | | N8N_TIMEOUT | No | Request timeout in ms (default: 30000) |

N8N_API_URL is also accepted as an alias for N8N_HOST.

Tools (15 total)

Reading

| Tool | Description | |------|-------------| | list_workflows | List all workflows (id, name, active, tags, node count) | | get_workflow | Full simplified workflow (all nodes with params, 80%+ fewer tokens) | | get_workflow_raw | Raw n8n JSON (debugging only, still strips worst bloat) | | scan_workflow | Focus Mode — Lightweight scan: names, types, 1-line summaries, segments, token estimate | | focus_workflow | Focus Mode — Full detail for selected nodes only, dormant summaries for the rest | | expand_focus | Focus Mode — Grow focus area by adding upstream/downstream/specific nodes |

Writing

| Tool | Description | |------|-------------| | create_workflow | Create from simplified format (auto-generates positions) | | update_workflow | Full replacement from simplified format (preserves positions/creds) | | update_nodes | Surgical operations: addNode, removeNode, updateNode, addConnection, removeConnection, enable, disable, rename | | delete_workflow | Permanent deletion (requires confirm: true) |

Activation & Execution

| Tool | Description | |------|-------------| | activate_workflow | Enable automatic triggers | | deactivate_workflow | Disable automatic triggers | | list_executions | List executions (filter by workflow, status) | | get_execution | Get execution details | | trigger_webhook | Trigger via webhook (production or test) |

Focus Mode

Focus Mode is designed for large workflows (30+ nodes) where loading everything at once wastes tokens. It's especially useful when workflows contain AI Agents with large system prompts, Code nodes with long scripts, or multiple Switch branches.

How It Works

scan_workflow     →  See the full structure (~2K-5K tokens, no params)
                     Detects branches, estimates total token cost
                     ↓
focus_workflow    →  Zoom into specific area (full detail inside, dormant outside)
                     3 modes: explicit nodes, branch, or range
                     ↓
update_nodes      →  Edit within the focus boundary
                     ↓
expand_focus      →  Need more context? Grow the boundary iteratively

Three Focus Modes

1. Explicit Nodes — Focus on specific nodes anywhere

focus_workflow({
  id: "workflow-id",
  nodes: ["AI Agent", "Parse Response", "Send Email"]
})

2. Branch — Auto-discover a Switch/IF branch

focus_workflow({
  id: "workflow-id",
  branch: {
    node: "Router",
    outputIndex: 2,
    includeUpstream: 1
  }
})

3. Range — Focus on everything between two nodes

focus_workflow({
  id: "workflow-id",
  range: {
    from: "Validate Input",
    to: "Send Response"
  }
})

Focus Output Structure

{
  "totalNodeCount": 75,
  "focused": [
    // Full LiteNode detail (params, creds, code) for selected nodes
  ],
  "focusedFlow": [
    // Connections between focused nodes only
  ],
  "dormant": [
    // Every other node: {name, type, zone, summary}
    {"name": "Webhook", "type": "webhook", "zone": "upstream", "summary": "Webhook: POST /incoming"},
    {"name": "Send Slack", "type": "slack", "zone": "downstream", "summary": "slack", "inputsFrom": ["AI Agent"]}
  ],
  "boundaries": [
    // Entry/exit points of the focus area
    {"from": "Validate", "to": "AI Agent", "direction": "entry"},
    {"from": "Parse Response", "to": "Send Slack", "direction": "exit"}
  ],
  "stats": {
    "focusedCount": 3,
    "upstreamCount": 5,
    "downstreamCount": 8,
    "parallelCount": 59
  }
}

Real-World Results

| Workflow | Nodes | get_workflow | scan_workflow | focus (2 nodes) | |----------|-------|-------------|---------------|-----------------| | CRIS Phase 2+3 | 38 | ~13,600 tok | ~2,800 tok (80% less) | ~2,600 tok (81% less) | | Limpex Atendimento | 78 | ~16,500 tok | ~5,100 tok (69% less) | ~3,100 tok (81% less) |

Simplified Format

Node Types

Prefixes are automatically stripped/restored:

| n8n Full Type | Simplified | |---|---| | n8n-nodes-base.httpRequest | httpRequest | | n8n-nodes-base.webhook | webhook | | @n8n/n8n-nodes-langchain.agent | langchain:agent | | @n8n/n8n-nodes-langchain.lmChatOpenAi | langchain:lmChatOpenAi |

LiteNode

{
  "name": "Send Email",
  "type": "gmail",
  "_id": "uuid",
  "_v": 2,
  "params": { "sendTo": "user@example.com", "subject": "Hello" },
  "creds": { "gmailOAuth2Api": "My Gmail" },
  "disabled": true,
  "onError": "continueRegularOutput",
  "notes": "Sends notification"
}

_id — Internal node ID (preserved for updates)
_v — Type version (only if != 1)
params — Only non-empty, non-default parameters
creds — Credential names only (IDs restored from original on update)
disabled — Only present if true
onError — Only if non-default

Connections

{"from": "Webhook", "to": "Process"}
{"from": "IF", "to": "True Path", "outputIndex": 0}
{"from": "IF", "to": "False Path", "outputIndex": 1}
{"from": "Switch", "to": "Case 3", "outputIndex": 2}
{"from": "Model", "to": "Agent", "type": "ai_languageModel"}
{"from": "Data", "to": "Merge", "inputIndex": 1}

update_nodes Operations

Surgical edits without sending the entire workflow:

update_nodes({
  id: "workflow-id",
  operations: [
    // Update parameters
    {op: "updateNode", name: "HTTP Request", params: {url: "https://new-api.com"}},

    // Add a node
    {op: "addNode", node: {name: "Filter", type: "filter", params: {...}}},

    // Connect/disconnect
    {op: "addConnection", from: "HTTP Request", to: "Filter"},
    {op: "removeConnection", from: "Old Node", to: "Filter"},

    // Remove node (also cleans up connections)
    {op: "removeNode", name: "Old Node"},

    // Enable/disable
    {op: "disable", name: "Debug Node"},
    {op: "enable", name: "Production Node"},

    // Rename (updates all connection references)
    {op: "rename", name: "Node 1", newName: "Validate Input"}
  ]
})

Building from Source

git clone https://github.com/LunkiBR/n8n-mcp-lite.git
cd n8n-mcp-lite
npm install
npm run build

Then use the "Local Development" config above, pointing to dist/index.js.

Architecture

src/
  index.ts              # MCP server entry point (stdio transport)
  api-client.ts         # n8n REST API client
  types.ts              # All type definitions
  transform/
    simplify.ts         # Raw JSON → Simplified format (bidirectional)
    graph.ts            # BFS, adjacency, classification algorithms
    focus.ts            # Scan, Focus, Dormant builders
  tools/
    definitions.ts      # 15 MCP tool schemas
    handlers.ts         # Tool handler implementations

License

MIT

MCP Servers

n8n-mcp-lite

The Problem

The Solution

Quick Start

1. Get your n8n API Key

2. Configure in your AI client

Claude Desktop

Claude Code

Cursor / Cline / Other MCP Clients

Local Development (from source)

3. Add the System Prompt (recommended)

Environment Variables

Tools (15 total)

Reading

Writing

Activation & Execution

Focus Mode

How It Works

Three Focus Modes

1. Explicit Nodes — Focus on specific nodes anywhere

2. Branch — Auto-discover a Switch/IF branch

3. Range — Focus on everything between two nodes

Focus Output Structure

Real-World Results

Simplified Format

Node Types

LiteNode

Connections

update_nodes Operations

Building from Source

Architecture

License

安装包（如果需要）

Cursor 配置 (mcp.json)

n8n-mcp-lite

The Problem

The Solution

Quick Start

1. Get your n8n API Key

2. Configure in your AI client

Claude Desktop

Claude Code

Cursor / Cline / Other MCP Clients

Local Development (from source)

3. Add the System Prompt (recommended)

Environment Variables

Tools (15 total)

Reading

Writing

Activation & Execution

Focus Mode

How It Works

Three Focus Modes

1. Explicit Nodes — Focus on specific nodes anywhere

2. Branch — Auto-discover a Switch/IF branch

3. Range — Focus on everything between two nodes

Focus Output Structure

Real-World Results

Simplified Format

Node Types

LiteNode

Connections

update_nodes Operations

Building from Source

Architecture

License

安装包 （如果需要）

Cursor 配置 (mcp.json)

安装包（如果需要）