Coupler Local MCP Server: Connect You Data to AI Tools

1. What is Coupler MCP?
2. Before You Start
3. Setting Up Coupler Local MCP
4. Understanding MCP Tools
5. Your First MCP Query: A Practical Example
6. Tips for Success
7. Getting Help

1. What is Coupler MCP?

The Coupler MCP (Model Context Protocol) Server is a bridge that connects your Coupler data flows directly to AI tools like Claude Desktop. Think of it as a secure tunnel that lets AI assistants access and analyze your business data without the usual copy-paste hassle.

Key Benefits

• No more manual exports – AI tools can access your data directly from Coupler.
• Handle large datasets – Analyze data that would normally exceed AI context limits through smart aggregation.
• Secure and private – Your data stays in your control, processed locally on your computer.
• Natural conversations – Ask questions about your data just like you would ask a colleague.

What You Can Do

With Coupler MCP, you can ask Claude questions like:

• "Show me my advertising performance for last month."
• "What are my top-performing products?"
• "Create a revenue forecast for the next quarter."
• "Analyze customer trends in my data."

Claude will fetch the data directly from your Coupler data flows and provide insights, create visualizations, and help you make data-driven decisions.

2. Before You Start

Prerequisites Checklist

1. Coupler Professional plan or higher – MCP is available starting from the Professional plan.
2. At least one data flow created from a template – MCP currently works only with template-based data flows. 
3. Successful data flow run – Your data flow must have completed at least one successful run.
4. Claude Desktop installed – The desktop application (not the web version).
5. Docker Desktop – Required to run the MCP server locally.

System Requirements

Both Docker Desktop and Claude Desktop have minimum system requirements. Please check their official documentation to ensure your computer meets these requirements.

3. Setting Up Coupler MCP (3 Simple Steps)

Setting up MCP is straightforward – follow these three steps to connect your data to Claude.

Step 1: Install Docker Desktop

Docker Desktop is required to run the MCP server on your computer.

1. Visit the Docker Desktop download page:
   • For Windows: Docker Desktop for Windows.
   • For Mac: Docker Desktop for Mac.
   • For Linux: Docker Desktop for Linux.
2. Download and install Docker Desktop following their installation guide.
3. Start Docker Desktop and ensure it's running (you'll see the Docker icon in your system tray).

Step 2: Get Your MCP Credentials

1. Log in to your Coupler.io account.
2. Navigate to the MCP section in the left navigation.

3. Select Claude Desktop as an AI tool.

4. Follow to the second step and generate the config.

5. Copy the entire configuration block that appears.

6. Keep this configuration handy – you'll need it in Step 3.

Step 3: Configure Claude Desktop

Now let's connect Claude to your Coupler data:

1. Open Claude Desktop (not the web version).

2. Navigate to Settings → Developer. If you use another OS, this section might be located elsewhere.

   

3. Click "Edit config". This will bring you to the folder where the config is stored.

   

4. Open a config with a text editor.

5. Paste the configuration you copied from Step 2. Ensure all curly braces are present as required by the config.

   

6. Save the configuration.
7. Restart Claude Desktop to activate the connection.

That's it! Claude can now access your Coupler data flows.

4. Understanding MCP Tools

When you chat with Claude about your data, it uses four main tools behind the scenes:

List Data Flows

What it does: Shows all your available template-based data flows.

When Claude uses it: When you ask, "What data do I have?" or "Show me my data flows."

What you'll see: A list of your data flow names and basic information. If you have more than 20 data flows created from templates, you will not see them all. However, you can still access the data using the data flow name.

Get Schema

What it does: Gets the structure of your data (column names and types).

When Claude uses it: Automatically with data reading to understand your data format.

What you'll see: Usually invisible – Claude uses this to properly interpret your data.

Get Data

What it does: Fetches the actual data from your data flow.

When Claude uses it: When you ask to analyze, visualize, or work with your data.

What you'll see: Claude will process the data and present insights based on your question.

5. Your First MCP Query: A Practical Example

Let's walk through a real example using e-commerce data.

This example assumes you have an e-commerce data flow created from a Coupler template.

Setting Up Your Claude Project (Recommended)

Before starting, we recommend creating a dedicated Claude project for data analysis:

1. In Claude Desktop, create a new project called "Data Analysis" or similar.

   

2. Add project instructions to help Claude understand your context.

   For example:

Role Definition
You are an experienced data analyst specializing in e-commerce, sales, and digital analytics. Your expertise spans customer behavior analysis, conversion optimization, revenue attribution, and performance measurement across multiple channels and platforms.

Core Competencies
E-commerce Analytics: Customer lifetime value, conversion funnels, cart abandonment, product performance
Sales Analytics: Pipeline analysis, lead scoring, attribution modeling, territory performance
Digital Marketing: Multi-channel attribution, campaign performance, audience segmentation, ROI analysis
Business Intelligence: KPI development, trend analysis, forecasting, executive reporting

Communication Style
Provide direct, concise responses without emotional language or embellishments
Focus on analytical insights and data-driven recommendations
Use precise terminology appropriate for analytics professionals
No emojis or conversational filler
Lead with findings, follow with brief context when necessary

Response Constraints
Provide analytical insights through text-based summaries and bullet points
Focus on data interpretation rather than data presentation
Limit your responses to 3 key metrics even if you have more things to say

3. You can ask Claude to provide more specific instructions tailored to your needs.

Step-by-Step Data Analysis

Query 1: "List my Coupler data flows"

Claude's response might look like:

Query 2: "Fetch my e-commerce sales data"

Claude will:

• Use the Get Schema and Get Data tools to understand and then fetch your sales data
• Load it into memory for analysis
• Confirm the data is ready

You might see:

Query 3: "What are my top countries over the last year?"

Claude will analyze your data and might create:

• A ranked list of countries from which you get the most orders or profits.
• An interactive bar chart (as a Claude artifact).
• Key insights about country performance.

Query 4: "Create a graph with my revenue projection for the next 6 months."

For this complex request, Claude might:

• Analyze historical trends in your data.
• Create a forecast model.
• Generate an interactive line chart showing:
  • Historical revenue.
  • Projected revenue.
  • Some additional information.

• Provide insights about growth trends and seasonality.

  

Working with Claude Artifacts

Claude can create interactive artifacts for your data, including:

• Charts and graphs – Interactive visualizations you can explore.
• Data tables – Sortable and filterable views of your data.
• Reports – Formatted documents you can save or share.
• Dashboards – Multi-chart views of your key metrics.

These artifacts appear in a separate panel in Claude Desktop, making it easy to interact with your visualizations while continuing the conversation.

6. Tips for Success

Best Practices

Start simple: Begin with basic questions like "What data do I have?" before moving to complex analysis.

Be specific: Instead of "analyze my data," try "show me sales trends for the last 3 months."

Use natural language: You don't need special commands – just ask Claude as you would ask a colleague.

Save important insights: Claude artifacts can be exported for use in presentations or reports.

Create projects for different analyses: Set up separate Claude projects for different types of analysis (sales, marketing, inventory) with tailored instructions.

Common Questions

"Why don't I see all my data flows?"

Only data flows created from templates with successful runs appear in MCP. Data flows built from scratch aren't currently supported. Besides, CLaude can only show you up to 20 data flows. If you have more than that, you can still access the data you are looking for using the name of the data flow.

"Can I modify data through MCP?"

No, MCP is read-only. You can analyze and visualize data, but not change it.

"How much data can I analyze?"

MCP uses smart aggregation to handle large datasets that would normally exceed AI context limits. You can work with data flows containing hundreds of thousands of rows.

"Can multiple people use the same MCP setup?"

Each user needs their own API token and MCP setup. Tokens are personal and shouldn't be shared.

When MCP is Most Useful

• Regular reporting: Ask Claude to create weekly or monthly summaries.
• Quick insights: Get instant answers without opening spreadsheets.
• Data exploration: Discover patterns you might miss manually.
• Presentation prep: Generate charts and insights for meetings.
• Forecasting: Use your historical data for predictions.

7. Getting Help

If you encounter any issues with Coupler MCP, please contact our support team.

We're here to help with:

• MCP setup and configuration.
• Understanding data access.
• Questions about features and capabilities.

Next Steps

Once you're comfortable with basic MCP usage:

1. Explore more complex analyses with your data.
2. Create Claude projects for different business areas.
3. Experiment with different types of visualizations.
4. Share your success stories with the team.

Remember, MCP is designed to make data analysis conversational and accessible. The more you use it, the more valuable insights you'll discover in your data.