# Matillion (Cloud Data Integration & ELT) MCP

> Matillion (Cloud Data Integration & ELT) gives your AI client direct control over complex data workflows. Audit pipelines, check execution statuses across multiple cloud environments like Snowflake and BigQuery, and manage all aspects of your enterprise ELT orchestration using natural conversation.

## Overview
- **Category:** brain-trust
- **Price:** Free
- **Tags:** elt-pipelines, data-integration, workflow-orchestration, data-transformation, cloud-data-warehouse

## Description

This MCP connects your AI agent directly to the Matillion Data Productivity Cloud. You take full command over your entire data integration lifecycle without needing to navigate complex dashboards or write API calls. Instead of logging into a web console just to check if 'Customer-360' ran successfully, you ask your agent and get an instant status report. It allows you to list every managed ETL pipeline, audit their structures, monitor active runtime agents on your local network, and even verify which cloud data warehouse environments are configured—whether that’s Redshift, BigQuery, or Snowflake. This level of infrastructure visibility is crucial for any serious data team. By connecting through Vinkius, you give your AI client the comprehensive view it needs to treat your entire data stack like one unified system.

## Tools

### list_pipelines
Lists all ETL pipelines managed within your Matillion instance.

### get_pipeline
Retrieves the detailed structural components for a single, specified pipeline ID.

### list_executions
Shows a list of recent data pipeline runs and their current status (success or failure).

### list_environments
Displays all configured cloud destination environments attached to the Matillion hub.

### list_agents
Enumerates and reports on the status of active Matillion runtime agents across your network.

### list_projects
Lists all major project containers that group related pipelines together for organization.

## Prompt Examples

**Prompt:** 
```
List all Matillion ETL pipelines in my account
```

**Response:** 
```
I've retrieved your ETL pipelines from the Data Productivity Cloud. Highlights include 'Sales-Data-Sync' (ID: pipe-123), 'Customer-360-View' (ID: pipe-456), and 'Finance-Consolidation' (ID: pipe-789). Would you like to see the structural components for the sales sync?
```

**Prompt:** 
```
Show me the last 5 pipeline executions and their status
```

**Response:** 
```
Retrieving recent executions… I've identified the 5 latest runs: 3 succeeded (including 'Inventory-Load'), 1 is currently 'Running' ('Marketing-Attribution'), and 1 'Failed' ('Web-Log-Ingestion'). Would you like me to investigate the error for the failed run?
```

**Prompt:** 
```
What cloud environments are configured in my Matillion instance?
```

**Response:** 
```
I've identified 3 destination environments: 'Snowflake-Prod' (Targeting: SF_WH_01), 'Redshift-Staging' (Targeting: AWS_NODE_ALPHA), and 'BigQuery-Analytics' (Targeting: GCP_DATASET_V1). I can provide the project mappings for these environments if you'd like.
```

## Capabilities

### View all ETL pipelines
List every managed pipeline container and its associated ID within Matillion.

### Deep-dive into pipeline structure
Retrieve the complete underlying orchestration definition for a single, specified data flow.

### Track run history and status
Monitor recent pipeline executions to see which ones succeeded, failed, or are currently running.

### List configured cloud destinations
Enumerate every data warehouse environment attached to the Matillion instance (e.g., Snowflake, BigQuery).

### Audit local runtime infrastructure
Check the operational status and count of all active Matillion runtime agents across your network.

### Manage project groupings
List high-level project containers that bind related pipelines and environments together for organization.

## Use Cases

### Debugging a sudden data outage
An engineer notices dashboard data is missing. They ask their agent to run list_executions for the 'Sales-Sync' pipeline, immediately identifying that the last run failed and retrieving specific error codes, solving the problem in seconds instead of hours.

### Preparing a new cloud destination
A BI lead needs to know if their new BigQuery dataset is ready. They ask the agent to list_environments, confirming the target credentials exist before running any data migration tests.

### Auditing compliance and governance
An operations analyst must prove that all transformation logic for a key dataset is documented. They use get_pipeline on the relevant ID to pull the full schema definition, satisfying audit requirements instantly.

### Scaling out data processing capacity
A team needs to confirm if their local compute resources are sufficient. They ask the agent to list_agents, checking the count and status of all runtime agents before initiating a large-scale batch job.

## Benefits

- Instant failure analysis. Instead of clicking into a dashboard to find out why data didn't move, use list_executions to see if the pipeline failed and get immediate status reports.
- Full infrastructure visibility. Easily audit your local network health by listing active agents with list_agents, ensuring that critical ELT processes can run without interruption.
- Data mapping accuracy checks. List all environments (list_environments) to confirm every destination—whether it’s Snowflake or Redshift—is correctly configured and mapped for the data.
- Effortless structure review. Need to know exactly what a pipeline does? Use get_pipeline to retrieve its full underlying orchestration definition without ever touching the Matillion UI.
- Project overview at a glance. List all projects (list_projects) so you can immediately understand the scope and boundaries of your data transformation efforts.

## How It Works

The bottom line is you use plain language to manage complex enterprise data infrastructure that usually requires dedicated developer tools.

1. Subscribe to this MCP on Vinkius.
2. Input your Matillion API URL, Client ID, and Client Secret into the connection settings.
3. Ask your AI client a question—like 'What are my active cloud environments?'—and it executes the necessary data calls.

## Frequently Asked Questions

**Can Matillion (Cloud Data Integration & ELT) MCP list all my pipelines?**
Yes. The list_pipelines tool gives you a complete inventory of every ETL pipeline container managed within your account, allowing you to see everything at a glance.

**How do I check if a pipeline failed using Matillion (Cloud Data Integration & ELT) MCP?**
Use list_executions. This tool retrieves recent run statuses and will clearly indicate which deployments succeeded, are running, or have outright failed.

**What is the difference between listing environments and checking agents with Matillion (Cloud Data Integration & ELT) MCP?**
list_environments shows where your data *goes* (Snowflake, BigQuery), while list_agents tells you about the actual local *compute power* that runs the job.

**Does Matillion (Cloud Data Integration & ELT) MCP help me with project grouping?**
Yes. The list_projects tool lets you view and understand how related pipelines and environments are grouped together within logical containers.

**Can I get the structure of a pipeline using Matillion (Cloud Data Integration & ELT) MCP?**
You can use get_pipeline. This tool doesn't just give you the name; it pulls the actual, deep structural definition of the data flow for detailed review.