n8n MCP Server Trigger Node

The MCP Server Trigger transforms n8n from an automation tool into an AI-accessible toolkit.

Instead of AI agents being limited to their built-in capabilities, they can now reach into n8n and use any tool you expose: database queries, API calls, file operations, custom workflows, and more.

The Shift in Control

This is fundamentally different from building workflows that use AI.

With the MCP Server Trigger, AI agents like Claude become the orchestrators. They decide which n8n tools to call, interpret the results, and take follow-up actions.

Your n8n instance becomes an extension of the AI’s capabilities.

Why MCP Matters for Automation

The Model Context Protocol (MCP) is an open standard that defines how AI applications connect to external tools and data sources.

Before MCP, every AI integration required custom code. Now, any MCP-compatible client can discover and use your tools through a standardized interface.

Think of it like USB for AI tools. Your n8n workflows become plug-and-play capabilities that any compatible AI agent can use without custom integration work.

What You’ll Learn

• When to use the MCP Server Trigger versus webhooks or direct API calls
• How MCP client-server architecture works
• Setting up your first MCP server with authentication
• Configuring Claude Desktop, Claude Code, and other MCP clients
• Transport protocol options: SSE vs streamable HTTP
• Reverse proxy configuration for production deployments
• Exposing n8n workflows as callable tools
• Troubleshooting common connection and authentication issues
• Real-world integration patterns and examples

When to Use the MCP Server Trigger

The MCP Server Trigger is specifically designed for AI agent integration. It differs from other trigger nodes in important ways. Use this table to determine if it’s the right choice for your use case.

Scenario	Use MCP Server Trigger?	Better Alternative
Claude Desktop needs to query your database	Yes	MCP provides standardized tool discovery
External service sends event notifications	No	Webhook node for standard webhooks
Your app needs to call n8n workflows via API	No	Webhook with HTTP authentication
AI agent needs multiple tools from n8n	Yes	MCP exposes multiple tools through one endpoint
Real-time chat with AI that uses n8n tools	Yes	MCP enables dynamic tool selection
Scheduled data processing	No	Schedule Trigger for time-based execution
Building an AI agent inside n8n	No	AI Agent node for internal agents

Rule of thumb: Use the MCP Server Trigger when an external AI agent needs to dynamically discover and call tools from your n8n instance. For everything else, webhooks or scheduled triggers are simpler.

MCP Server Trigger vs Webhook

New users often confuse these nodes:

• MCP Server Trigger: Exposes tools to MCP clients (AI agents). Clients can list available tools and call them individually. Uses SSE or streamable HTTP transport.
• Webhook node: Receives HTTP requests from any service. Single endpoint, single purpose. Standard request/response pattern.

The key difference is discoverability. MCP clients can ask “what tools do you have?” and get a structured response. Webhooks are just endpoints that accept requests.

MCP Server Trigger vs AI Agent Node

These nodes work in opposite directions:

• MCP Server Trigger: Makes n8n tools available to external AI agents
• AI Agent node: Runs AI agents inside n8n that can use tools

You might use both together. An external Claude agent connects via MCP to call an n8n workflow that itself uses an internal AI agent for complex reasoning. The MCP Server Trigger is the bridge that makes this possible.

MCP Server Trigger vs MCP Client Tool

n8n supports MCP in both directions:

• MCP Server Trigger: n8n acts as an MCP server, exposing tools to external AI agents
• MCP Client Tool: n8n acts as an MCP client, connecting to external MCP servers

Use the MCP Client Tool when you want your n8n AI Agent to access tools from external MCP servers (like filesystem access, database queries from other systems, or third-party MCP services). Use the MCP Server Trigger when external AI agents need to access your n8n tools.

MCP Server Trigger vs n8n’s Built-in MCP Server

This distinction causes confusion. n8n has two separate MCP capabilities:

MCP Server Trigger Node (this guide):

• You create a workflow with this trigger node
• You choose exactly which tools to expose
• You configure custom authentication
• Endpoint path is based on your workflow configuration
• Best for: Custom tool sets, specific use cases, controlled access

n8n’s Built-in MCP Server:

• Automatically available at /mcp-server/http on your n8n instance
• Exposes n8n’s internal API as MCP tools (list workflows, execute workflows, manage instance)
• Uses n8n API authentication
• No workflow configuration needed
• Best for: AI agents that need to manage n8n itself, execute arbitrary workflows

If you want AI agents to use specific tools you’ve designed, use the MCP Server Trigger. If you want AI agents to interact with n8n as a platform (listing and running any workflow), use the built-in MCP server.

Understanding MCP Architecture

Before diving into configuration, understanding how MCP works helps you design better integrations and debug issues faster.

The Client-Server Model

MCP uses a client-server architecture:

MCP Server (n8n with MCP Server Trigger):

• Exposes tools, resources, and prompts
• Handles authentication
• Executes requested tool calls
• Returns results to clients

MCP Client (Claude Desktop, Claude Code, custom agents):

• Discovers available tools from the server
• Decides which tools to call based on user requests
• Sends tool invocations to the server
• Processes results and continues reasoning

When you add an MCP Server Trigger to your n8n workflow, you’re creating an MCP server that AI clients can connect to.

How Tool Discovery Works

When an MCP client connects to your n8n MCP server, it first requests a list of available tools. Each tool includes:

• Name: Unique identifier for the tool
• Description: What the tool does (crucial for AI decision-making)
• Input schema: What parameters the tool accepts
• Output format: What the tool returns

The AI client uses this information to decide when and how to use each tool. Clear, specific descriptions lead to better tool selection by the AI.

Transport Protocols

The MCP Server Trigger supports two transport mechanisms for communication between clients and your n8n server:

Server-Sent Events (SSE)

SSE is a long-lived HTTP connection that allows the server to push events to the client. It’s built on standard HTTP but maintains a persistent connection.

• Pros: Works through most firewalls, simple to implement
• Cons: Can break with misconfigured reverse proxies, requires specific server configuration

Streamable HTTP

A more robust transport option that handles connections more gracefully. Better suited for production environments with complex network configurations.

• Pros: More reliable through proxies, better error recovery
• Cons: Slightly more complex client configuration

Most clients support both. Start with streamable HTTP for production deployments; use SSE for development and testing.

URL Types: Test vs Production

Like the Webhook node, the MCP Server Trigger generates two distinct URLs. Understanding the difference prevents the most common configuration mistakes.

Test URL Behavior

The test URL activates when you’re working in the n8n editor:

1. Open your workflow containing the MCP Server Trigger
2. Click “Listen for Test Event” on the node
3. n8n registers a temporary MCP endpoint
4. You have 120 seconds to connect and test
5. Data appears in the editor for debugging

Test URLs include /mcp-test/ in the path. They’re perfect for development but disappear when you close the editor.

Production URL Behavior

Production URLs work when your workflow is activated:

1. Configure your MCP Server Trigger node
2. Save the workflow
3. Toggle the workflow to “Active”
4. n8n registers a persistent MCP endpoint
5. Clients can connect anytime the workflow is active

Production URLs include /mcp/ in the path. Configure your MCP clients with this URL for reliable connections.

Common URL Mistakes

Mistake	Problem	Solution
Using test URL in client config	Connection fails when editor closes	Switch to production URL and activate workflow
Workflow not activated	Production URL returns 404	Toggle workflow to Active
HTTP instead of HTTPS	Clients may reject insecure connections	Use HTTPS in production
Wrong URL path copied	Connection fails silently	Verify exact URL from node panel

Your First MCP Server

Let’s build a working MCP server that exposes a simple tool to AI clients.

Step 1: Add the MCP Server Trigger Node

1. Create a new workflow in n8n
2. Click + to add a node
3. Search for “MCP Server Trigger”
4. Click to add it as your trigger

The node appears with the MCP URL displayed at the top of the panel.

Step 2: Configure Authentication

MCP endpoints should always require authentication in production. Click on the node to configure:

1. Set Authentication to “Bearer”
2. Click Create New Credential
3. Enter a strong token value (generate one with a password manager)
4. Save the credential

This token becomes required for all client connections. Anyone without the token cannot access your tools.

Step 3: Add a Tool to Expose

The MCP Server Trigger connects to tool nodes, not regular workflow nodes. Let’s add a simple calculator tool:

1. Click the + connector from the MCP Server Trigger
2. Search for “Calculator”
3. Select the Calculator tool node

Your AI clients can now perform mathematical calculations through your MCP server.

Step 4: Test the Connection

1. Copy the production MCP URL from the node panel
2. Activate the workflow
3. Configure a client (we’ll cover Claude Desktop next)
4. Ask the AI to perform a calculation

If everything is configured correctly, the AI will call your n8n calculator tool and return the result.

Expanding Your Tool Set

Add more tools by connecting additional tool nodes to the MCP Server Trigger:

• HTTP Request Tool: Call any API
• Code Tool: Execute custom JavaScript or Python
• Workflow Tool: Trigger other n8n workflows
• Database tools: Query your data stores

Each connected tool becomes available to MCP clients automatically.

Authentication Methods

Protecting your MCP endpoint is critical. Anyone with access can invoke your tools, potentially causing data changes or triggering expensive operations.

Bearer Token Authentication

The most common and recommended method:

1. In the MCP Server Trigger node, set Authentication to “Bearer”
2. Create a credential with your token value
3. Clients include the token in their connection configuration

Client configuration example:

Authorization: Bearer your-secure-token-here

Generate tokens using cryptographically secure random generators. Avoid simple passwords or predictable values.

Header Authentication

For clients that need custom header names:

1. Set Authentication to “Header Auth”
2. Configure the header name (e.g., X-API-Key)
3. Set the header value

Client configuration example:

X-API-Key: your-secure-value-here

Use this when integrating with systems that expect specific header formats.

Authentication Comparison

Method	Security	Ease of Use	Best For
None	Low	Trivial	Local testing only
Bearer Token	High	Easy	Most MCP clients
Header Auth	High	Medium	Custom integrations

Never use “None” in production. Even for internal tools, authentication prevents accidental exposure and provides audit trails.

For comprehensive authentication troubleshooting, see our guide to fixing n8n authentication errors.

Connecting AI Clients

Different MCP clients require specific configuration formats. Here’s how to connect the most popular ones to your n8n MCP server.

Claude Desktop

Claude Desktop is Anthropic’s desktop application that supports MCP connections. Because Claude Desktop uses stdio-based communication internally, you need a proxy to connect to n8n’s HTTP-based MCP server.

Configuration file location:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Configuration example:

{
  "mcpServers": {
    "n8n": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://your-n8n-instance.com/mcp/your-endpoint-path",
        "--header",
        "Authorization: Bearer ${AUTH_TOKEN}"
      ],
      "env": {
        "AUTH_TOKEN": "your-bearer-token-here"
      }
    }
  }
}

Replace:

• your-n8n-instance.com/mcp/your-endpoint-path with your production MCP URL
• your-bearer-token-here with your authentication token

The mcp-remote package acts as a bridge between Claude Desktop’s stdio transport and n8n’s HTTP transport.

Claude Code (CLI)

Claude Code can connect directly to HTTP MCP servers without a proxy.

Method 1: CLI command

claude mcp add --transport http n8n-mcp https://your-n8n-instance.com/mcp/your-endpoint-path \
  --header "Authorization: Bearer your-token-here"

Method 2: Configuration file

Add to your Claude Code configuration:

{
  "mcpServers": {
    "n8n-mcp": {
      "type": "http",
      "url": "https://your-n8n-instance.com/mcp/your-endpoint-path",
      "headers": {
        "Authorization": "Bearer your-token-here"
      }
    }
  }
}

Codex CLI

Codex CLI uses TOML configuration format:

[mcp_servers.n8n_mcp]
command = "npx"
args = [
    "-y",
    "supergateway",
    "--streamableHttp",
    "https://your-n8n-instance.com/mcp/your-endpoint-path",
    "--header",
    "authorization:Bearer your-token-here"
]

Google ADK

For Google’s Agent Development Kit, configure the MCP connection in Python:

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPServerParams

N8N_INSTANCE_URL = "https://your-n8n-instance.com"
N8N_MCP_TOKEN = "your-bearer-token-here"

root_agent = Agent(
    model="gemini-2.5-pro",
    name="n8n_agent",
    instruction="Help users with tasks using n8n tools",
    tools=[
        McpToolset(
            connection_params=StreamableHTTPServerParams(
                url=f"{N8N_INSTANCE_URL}/mcp/your-endpoint-path",
                headers={
                    "Authorization": f"Bearer {N8N_MCP_TOKEN}",
                },
            ),
        )
    ],
)

Custom MCP Clients

If you’re building your own MCP client, connect using standard HTTP:

1. Discover tools: GET /mcp/your-endpoint-path with authentication headers
2. Call tools: POST /mcp/your-endpoint-path with tool name and arguments
3. Handle responses: Parse JSON responses containing tool results

The exact protocol details are documented in the official MCP specification.

Transport Protocols and Reverse Proxy Configuration

Production deployments often place n8n behind a reverse proxy like nginx, Caddy, or Traefik. SSE and streamable HTTP connections require specific proxy configuration to work correctly.

The Proxy Buffering Problem

By default, most reverse proxies buffer responses before sending them to clients. This breaks SSE connections because events never reach the client until the connection closes.

Symptoms of proxy buffering issues:

• Connections hang with no response
• Tools appear to work but responses never arrive
• Intermittent connection drops

nginx Configuration for MCP

Add this configuration to your nginx server block for the MCP endpoint:

location /mcp/ {
    proxy_http_version          1.1;
    proxy_buffering             off;
    gzip                        off;
    chunked_transfer_encoding   off;

    proxy_set_header            Connection '';
    proxy_set_header            Host $host;
    proxy_set_header            X-Real-IP $remote_addr;
    proxy_set_header            X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header            X-Forwarded-Proto $scheme;

    proxy_pass                  http://localhost:5678;
}

Key settings explained:

• proxy_buffering off: Disables response buffering, essential for SSE
• gzip off: Prevents compression that can delay events
• chunked_transfer_encoding off: Ensures events flow immediately
• Connection '': Removes connection headers that can interfere with SSE

For more reverse proxy guidance, see nginx’s official documentation.

Caddy Configuration

Caddy handles SSE better by default, but explicit configuration helps:

your-n8n-domain.com {
    reverse_proxy localhost:5678 {
        flush_interval -1
    }
}

The flush_interval -1 setting ensures immediate event delivery.

Troubleshooting Transport Issues

Symptom	Likely Cause	Solution
Connection hangs	Proxy buffering enabled	Disable buffering in proxy config
Frequent disconnects	Keep-alive timeout too short	Increase proxy timeout values
Works locally, fails remotely	Firewall blocking long-lived connections	Allow persistent HTTP connections
Partial events received	Compression enabled	Disable gzip for MCP endpoints

If you’re running n8n with webhook replicas, route all /mcp* requests to a single dedicated replica. SSE connections are stateful and must reach the same server instance.

Exposing Tools and Workflows

The MCP Server Trigger exposes tools, not arbitrary workflow outputs. Understanding what can be exposed helps you design effective AI integrations.

What Gets Exposed

When you connect tool nodes to the MCP Server Trigger, each becomes an available tool:

• Tool name: Derived from the node name (customize for clarity)
• Tool description: From the node’s description field
• Input parameters: Based on the tool’s input configuration
• Output format: Determined by what the tool returns

Clear, specific tool names and descriptions improve AI tool selection. Instead of “HTTP Request”, use “QueryCustomerDatabase” or “SendSlackNotification”.

Custom n8n Workflow Tool

To expose entire n8n workflows as tools, use the Custom n8n Workflow Tool node:

1. Create a workflow you want to expose (e.g., a customer lookup workflow)
2. Add an Execute Workflow Trigger to that workflow
3. In your MCP workflow, add a Custom n8n Workflow Tool
4. Configure it to call your target workflow
5. Define clear input parameters and descriptions

This pattern lets you expose complex multi-step automations as single tools that AI agents can call.

Tool Description Best Practices

AI agents decide which tools to use based on descriptions. Well-written descriptions lead to accurate tool selection:

Weak description:

Get data from the database

Strong description:

Query the customer database by email address.
Input: Customer email (required).
Output: Customer profile including name, account status,
order history, and support tickets. Returns empty if
customer not found.

Include:

• What the tool does
• Required vs optional inputs
• What the output contains
• Edge cases and limitations

Selective Tool Exposure

Not every tool in your n8n instance should be exposed to AI agents. Design your MCP workflow to expose only what’s needed:

• Create a dedicated workflow for MCP with specific tools
• Don’t connect sensitive tools (delete operations, admin functions)
• Consider separate MCP endpoints for different access levels
• Use authentication to control who can access which tools

Common Issues and Troubleshooting

These issues appear frequently in community forums and support channels. Knowing the solutions saves debugging time.

Issue 1: Connection Refused or Timeout

Symptoms: Client can’t establish connection. Timeout errors or connection refused.

Diagnosis:

1. Verify the workflow is activated (not just saved)
2. Confirm you’re using the production URL (not test URL)
3. Check that n8n is accessible from the client’s network
4. Verify firewall rules allow connections on the n8n port

Quick test: Try accessing your n8n instance directly in a browser. If that fails, the issue is network-level.

Issue 2: Authentication Failed (401 Errors)

Symptoms: Client connects but receives 401 Unauthorized.

Diagnosis:

1. Token mismatch - verify exact token in client config matches n8n credential
2. Header format wrong - ensure proper Bearer prefix for bearer auth
3. Credential not saved - re-save the credential in n8n

Common mistake: Including quotes around the token in configuration files when they shouldn’t be there.

Issue 3: Tools Not Appearing to Client

Symptoms: Connection succeeds but AI says no tools available.

Diagnosis:

1. Verify tool nodes are connected to the MCP Server Trigger
2. Check that connected nodes are actually tool nodes (not regular nodes)
3. Refresh the client’s tool cache if it supports that
4. Test with a simple tool like Calculator first

Issue 4: SSE Connection Drops

Symptoms: Connection works briefly then drops. Events stop arriving.

Diagnosis:

1. Reverse proxy buffering - see nginx configuration section above
2. Load balancer timeout - increase idle timeout settings
3. Multiple webhook replicas - route MCP to single instance

Issue 5: Tool Execution Hangs

Symptoms: Tool is called but never returns a result.

Diagnosis:

1. The underlying tool may be failing - check n8n execution logs
2. Timeout too short - increase tool execution timeout
3. Tool waiting for unavailable resource - check database connections, API availability

For debugging complex issues, our workflow debugger tool can help identify where problems occur.

Real-World Examples

Example 1: Customer Support AI with CRM Access

Scenario: A Claude-based support agent needs to look up customer information and create support tickets.

MCP Server configuration:

• Authentication: Bearer token
• Tools exposed:
  • CustomerLookup (HTTP Request tool calling CRM API)
  • CreateTicket (Workflow tool calling ticket creation workflow)
  • OrderHistory (HTTP Request tool calling orders API)

Tool descriptions:

CustomerLookup:
Look up customer by email. Returns customer profile including
name, account tier, and account status. Use this before
answering questions about a specific customer's account.

CreateTicket:
Create a support ticket. Required inputs: customer_email,
subject, description, priority (low/medium/high).
Use when customer needs follow-up or issue can't be
resolved immediately.

OrderHistory:
Get customer's order history. Input: customer_email.
Returns last 10 orders with date, items, and status.

Result: The AI can handle support inquiries end-to-end, accessing real customer data and taking action when needed.

Example 2: Development Assistant with GitHub Tools

Scenario: A coding assistant needs to interact with your GitHub repositories.

MCP Server configuration:

• Authentication: Header auth with X-Developer-Key
• Tools exposed:
  • ListRepositories (HTTP Request to GitHub API)
  • CreateIssue (Workflow tool for issue creation with templates)
  • GetRepoInfo (HTTP Request for repository details)

Integration pattern:

Developer asks about project → AI lists repos →
Picks relevant repo → Gets detailed info →
Creates issue if needed

This extends the AI’s capabilities beyond its training data to your specific projects and workflows.

Example 3: Research Agent with Web Search

Scenario: An AI research assistant that can search the web and access internal documentation.

MCP Server configuration:

• Authentication: Bearer token
• Tools exposed:
  • WebSearch (HTTP Request to SerpAPI or similar)
  • SearchDocs (HTTP Request to internal documentation API)
  • SaveNote (Workflow tool to save research notes)

Key consideration: Rate limit the WebSearch tool to prevent runaway API costs. Implement this in the workflow logic or through the search API configuration.

For more complex AI integrations, see our guides on AI agents vs LLM chains and multi-agent orchestration.

Pro Tips for Production MCP Servers

1. Version Your MCP Endpoints

When tools change, client behavior changes. Maintain versioned endpoints:

/mcp/v1/customer-tools
/mcp/v2/customer-tools

Migrate clients gradually rather than breaking existing integrations.

2. Implement Rate Limiting

AI agents can be aggressive with tool calls. Add rate limiting at the proxy level or within n8n:

limit_req_zone $binary_remote_addr zone=mcp_limit:10m rate=30r/m;

location /mcp/ {
    limit_req zone=mcp_limit burst=10 nodelay;
    # ... rest of config
}

This prevents runaway costs from chatty AI agents.

3. Log Tool Invocations

Add logging to understand how AI agents use your tools:

// In a Code node after tool execution
const logEntry = {
  timestamp: new Date().toISOString(),
  tool: $json.toolName,
  input: $json.input,
  executionTime: $json.executionTime,
  success: !$json.error
};
// Send to your logging service

Logs reveal usage patterns and help optimize tool descriptions.

4. Use Descriptive Error Messages

When tools fail, return useful error messages that help the AI recover:

Bad: "Error"
Good: "Customer not found. Verify email address format
(example: user@domain.com) and try again."

The AI can use detailed errors to provide better user guidance.

5. Test with Multiple Clients

Different MCP clients may interpret tool responses differently. Test your server with:

• Claude Desktop
• Claude Code
• Your target production client

Ensure consistent behavior across all clients you need to support.

6. Separate Development and Production

Maintain separate MCP endpoints:

• Development: Relaxed rate limits, detailed logging
• Staging: Production-like with safe data
• Production: Full security, monitoring

For comprehensive workflow architecture guidance, explore our workflow best practices guide or consider our consulting services for complex implementations.

Frequently Asked Questions

How is the MCP Server Trigger different from a regular webhook?

The MCP Server Trigger implements the Model Context Protocol, which is specifically designed for AI agent integration.

Unlike a webhook that receives a single request and returns a single response, MCP provides:

• Tool discovery: AI clients can query what tools are available and their schemas
• Multiple invocations: Supports multiple tool calls in a single session
• Streaming transport: Uses SSE or streamable HTTP for real-time communication

Think of webhooks as “call this endpoint with data” and MCP as “here are the tools available, call whichever you need.”

MCP enables AI agents to dynamically discover and use your n8n capabilities without hardcoded integration.

Can I expose any n8n node through the MCP Server Trigger?

No, the MCP Server Trigger only connects to tool nodes, not regular workflow nodes.

Tool nodes in n8n are specifically designed to be callable by AI systems:

• Calculator
• Code
• HTTP Request Tool
• Workflow Tool

If you want to expose complex workflow logic, create that workflow separately and connect it using the Custom n8n Workflow Tool node.

This design ensures that exposed tools have clear inputs, outputs, and descriptions that AI agents can understand and use correctly.

Why does my Claude Desktop connection keep dropping?

The most common causes are reverse proxy buffering and network timeouts.

Check these first:

1. If running n8n behind nginx or another proxy, ensure you’ve disabled buffering with proxy_buffering off
2. Verify proxy timeouts are long enough for SSE connections, which can remain open for extended periods
3. If using multiple webhook replicas in n8n, route all MCP requests to a single dedicated replica (SSE connections are stateful)

Check our transport protocols section above for complete nginx configuration.

Do I need to expose every tool to all MCP clients?

No, and you probably shouldn’t.

Create separate MCP Server Trigger workflows for different access levels or use cases:

• A customer support AI might get access to CustomerLookup and CreateTicket tools
• A developer AI gets access to CodeReview and DeployPreview tools

Use different authentication tokens for different access levels and log which tools are called by which clients.

This follows the principle of least privilege and makes your MCP architecture more secure and maintainable.

How do I debug when an AI agent isn’t using my tools correctly?

Start by checking your tool descriptions. AI agents decide which tools to use based on descriptions, so vague or misleading descriptions cause incorrect tool selection.

Debugging steps:

1. Test with explicit prompts like “Use the CustomerLookup tool to find customer X” to verify the tool works
2. Check n8n execution logs to see if tools are being called and what they return
3. Enable detailed logging in your MCP workflow to capture tool invocations and responses
4. If the AI consistently chooses the wrong tool, improve the tool description to be more specific

For complex debugging, our workflow debugger tool can help trace execution flow.

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Need help implementing complex MCP integrations or building AI-powered automation? Explore our workflow development services for expert assistance.