What You'll Build

An AI-powered data pipeline development system that uses Continue’s AI agent with dlt MCP to inspect pipeline execution, retrieve schemas, analyze datasets, and debug load errors - all through simple natural language prompts

Prerequisites

Before starting, ensure you have: For all options, first:
1

Install Continue CLI

npm i -g @continuedev/cli
2

Install dlt

pip install dlt
To use agents in headless mode, you need a Continue API key.

dlt MCP Workflow Options

🚀 Fastest Path to Success

Skip the manual setup and use our pre-built dlt Assistant agent that includes the dlt MCP and optimized data pipeline workflows for more consistent results. You can remix this agent to customize it for your specific needs.
After ensuring you meet the Prerequisites above, you have two paths to get started:
To use the pre-built dlt Assistant agent, you need either:
  • Continue CLI Pro Plan with the models add-on, OR
  • Your own API keys added to Continue Hub secrets (same as manual setup)
The agent will automatically detect and use your configuration along with the pre-configured dlt MCP for pipeline operations.

dlt MCP vs dlt+ MCP

Understanding the Difference

dlt MCP is focused on local pipeline development and inspection. It provides tools to:
  • Inspect pipeline execution and load information
  • Retrieve schema metadata from your local pipelines
  • Query dataset records from destination databases
  • Analyze load errors, timings, and file sizes
dlt+ MCP extends these capabilities with cloud-based features for production deployments:
  • Connect to dlt+ Projects and manage deployments
  • Monitor pipeline runs across multiple environments
  • Access centralized logging and observability
  • Collaborate with team members on pipeline development
For local development and getting started, dlt MCP is the right choice. Consider dlt+ MCP when you need production deployment features and team collaboration.

Pipeline Development Recipes

Now you can use natural language prompts to develop and debug your dlt pipelines. The Continue agent automatically calls the appropriate dlt MCP tools.
You can add prompts to your agent’s configuration for easy access in future sessions. Go to your agent in the Continue Hub, click Edit, and add prompts under the Prompts section.
Where to run these workflows:
  • IDE Extensions: Use Continue in VS Code, JetBrains, or other supported IDEs
  • Terminal (TUI mode): Run cn to enter interactive mode, then type your prompts
  • CLI (headless mode): Use cn -p "your prompt" for headless commands
Test in Plan Mode First: Before running pipeline operations that might make changes, test your prompts in plan mode (see the Plan Mode Guide; press Shift+Tab to switch modes in TUI/IDE). This shows you what the agent will do without executing it.
About the —auto flag: The --auto flag enables tools to run continuously without manual confirmation. This is essential for headless mode where the agent needs to execute multiple tools automatically to complete tasks like pipeline inspection, schema retrieval, and error analysis.

Pipeline Inspection

Inspect Pipeline Execution

Review pipeline execution details including load timing and file sizes.TUI Mode Prompt:
Inspect my dlt pipeline execution and provide a summary of the load info.
Show me the timing breakdown and file sizes for each table.
Headless Mode Prompt:
cn -p "Inspect my dlt pipeline execution and provide a summary of the load info. Show me the timing breakdown and file sizes for each table." --auto

Schema Management

Retrieve Schema Metadata

Get detailed schema information for your pipeline’s tables.TUI Mode Prompt:
Show me the schema for my users table including all columns,
data types, and any constraints.
Headless Mode Prompt:
cn -p "Show me the schema for my users table including all columns, data types, and any constraints." --auto

Data Exploration

Query Dataset Records

Retrieve and analyze records from your destination database.TUI Mode Prompt:
Get the last 10 records from my orders table and show me
the distribution of order statuses.
Headless Mode Prompt:
cn -p "Get the last 10 records from my orders table and show me the distribution of order statuses." --auto

Error Debugging

Analyze Load Errors

Investigate and understand pipeline load errors.TUI Mode Prompt:
Check for any load errors in my last pipeline run. If there are errors,
explain what went wrong and suggest fixes.
Headless Mode Prompt:
cn -p "Check for any load errors in my last pipeline run. If there are errors, explain what went wrong and suggest fixes." --auto

Pipeline Creation

Build New Pipeline

Create a new dlt pipeline from an API or data source.TUI Mode Prompt:
Help me create a new dlt pipeline that loads data from the
JSONPlaceholder API users endpoint into DuckDB.
Headless Mode Prompt:
cn -p "Help me create a new dlt pipeline that loads data from the JSONPlaceholder API users endpoint into DuckDB." --auto

Schema Evolution

Handle Schema Changes

Review and manage schema evolution in your pipelines.TUI Mode Prompt:
Check if my pipeline schema has evolved since the last run.
Show me what columns were added or modified.
Headless Mode Prompt:
cn -p "Check if my pipeline schema has evolved since the last run. Show me what columns were added or modified." --auto

Continuous Data Pipelines with GitHub Actions

This example demonstrates a Continuous AI workflow where data pipeline validation runs automatically in your CI/CD pipeline in headless mode using the dlt Assistant agent. Consider remixing this agent to add your organization’s specific validation rules.

Add GitHub Secrets

Navigate to Repository Settings → Secrets and variables → Actions and add:
The workflow uses the pre-built dlt Assistant agent with --config dlthub/dlt-assistant. This agent comes pre-configured with the dlt MCP and optimized rules for pipeline operations. You can remix this agent to customize the validation rules and prompts for your specific pipeline requirements.

Create Workflow File

This workflow automatically validates your dlt data pipelines on pull requests using the Continue CLI in headless mode. It inspects pipeline schemas, checks for errors, and posts a summary report as a PR comment. The workflow can also be triggered manually via workflow_dispatch. Create .github/workflows/dlt-pipeline-validation.yml in your repository:
name: Data Pipeline Validation with dlt MCP

on:
  pull_request:
    branches: [main]
  workflow_dispatch:

jobs:
  validate-pipeline:
    runs-on: ubuntu-latest
    env:
      CONTINUE_API_KEY: ${{ secrets.CONTINUE_API_KEY }}

    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"

      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
          node-version: "18"

      - name: Install dlt
        run: |
          pip install dlt
          echo "✅ dlt installed"

      - name: Install Continue CLI
        run: |
          npm install -g @continuedev/cli
          echo "✅ Continue CLI installed"

      - name: Validate Pipeline Schema
        run: |
          echo "🔍 Validating pipeline schema..."
          cn --config dlthub/dlt-assistant \
             -p "Inspect the pipeline schema and verify all required tables
                 and columns are present. Flag any missing or unexpected changes." \
             --auto

      - name: Check Pipeline Health
        run: |
          echo "📊 Checking pipeline health..."
          cn --config dlthub/dlt-assistant \
             -p "Analyze the last pipeline run for errors or warnings.
                 Report any issues that need attention." \
             --auto

      - name: Comment Pipeline Report on PR
        if: always() && github.event_name == 'pull_request'
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          REPORT=$(cn --config dlthub/dlt-assistant \
             -p "Generate a concise summary (200 words or less) of:
                 - Pipeline schemas and row counts
                 - Any load errors or warnings
                 - Performance metrics (timing, file sizes)
                 - Recommended improvements" \
             --auto)

          gh pr comment ${{ github.event.pull_request.number }} --body "$REPORT"
The dlt MCP works with your local pipeline state. Make sure your CI environment has access to the necessary pipeline configuration and credentials.

Pipeline Development Best Practices

Implement automated pipeline quality checks using Continue’s rule system. See the Rules deep dive for authoring tips.

Schema Validation

"Before committing pipeline changes, verify the schema
matches expectations and flag any unexpected modifications."

Error Handling

"When load errors occur, analyze the error details and
suggest specific code fixes to handle the data issues."

Performance Monitoring

"Track pipeline execution times and file sizes. Alert if
performance degrades significantly from baseline."

Data Quality

"After each pipeline run, validate row counts and check for
null values in critical columns."

Troubleshooting

Pipeline Not Found

"Check if there's a dlt pipeline in the current directory.
If not, help me initialize a new pipeline."

Destination Connection Issues

"Verify the destination connection and credentials for my pipeline.
Test the connection and report any issues."

Schema Inference Problems

Verification Steps:
  • dlt MCP is installed via Continue Hub
  • Pipeline directory is accessible
  • Destination database credentials are configured
  • Pipeline has been run at least once

What You’ve Built

After completing this guide, you have a complete AI-powered data pipeline development system that: ✅ Uses natural language — Simple prompts instead of complex pipeline commands ✅ Debugs automatically — AI analyzes errors and suggests fixes ✅ Runs continuously — Automated validation in CI/CD pipelines ✅ Ensures quality — Pipeline checks prevent bad data from shipping

Continuous AI

Your data pipeline workflow now operates at Level 2 Continuous AI - AI handles routine pipeline inspection and debugging with human oversight through review and approval of changes.

Next Steps

  1. Inspect your first pipeline - Try the pipeline inspection prompt on your current project
  2. Debug load errors - Use the error analysis prompt to fix any issues
  3. Set up CI pipeline - Add the GitHub Actions workflow to your repo
  4. Create new pipelines - Use AI to scaffold new data sources
  5. Monitor performance - Track pipeline execution metrics over time

Additional Resources