# Metaplane MCP

> Metaplane connects data observability directly into your AI client. Get full control over data quality by tracking monitors, incident history, and system health through natural conversation. You can check connection schemas, list all active alerts, or trigger a manual monitor run without leaving your chat interface.

## Overview
- **Category:** data-analytics
- **Price:** Free
- **Tags:** data-quality, data-observability, incident-management, data-pipelines, monitoring, alerts

## Description

This MCP lets you manage data quality and understand the status of your pipelines using only conversation. Instead of logging into Metaplane's dashboard and clicking through multiple tabs to see if everything's running okay, you just ask your AI client. You can pull a list of all configured monitors or fetch details on a specific incident that popped up last night. Need to validate data quality right now? Your agent triggers a monitor run for you. Furthermore, you can view the connection schemas across different databases and even check active alert rules. When you connect this through Vinkius, your AI client becomes your single pane of glass for all things data observability.

## Tools

### get_account_info
Retrieves general account information for your Metaplane setup.

### get_incident
Fetches detailed records and status updates for one specific data quality incident.

### get_monitor
Gets comprehensive details about a single configured data monitor.

### get_monitor_runs
Retrieves the execution history and results for a specific data monitor.

### list_configured_alerts
Lists all active notification settings and alert rules configured in Metaplane.

### list_data_connections
Enumerates every connected data source, warehouse, and database within your account.

### list_incidents
Generates a list of historical or active data incidents across the entire system.

### list_monitors
Provides an overview and list of every configured data monitor currently set up.

### list_connection_schemas
Lists all available schemas within a specified data connection or warehouse.

### trigger_monitor_run
Manually initiates a monitor run to check the current data quality against defined rules.

## Prompt Examples

**Prompt:** 
```
List all my data monitors in Metaplane.
```

**Response:** 
```
Retrieving monitors... I found 5 active monitors including 'Postgres Row Count' and 'Snowflake Schema Change'.
```

**Prompt:** 
```
Show recent incidents for the last 24 hours.
```

**Response:** 
```
Checking incidents... There is one active high-severity incident: 'Null values detected in production.users'.
```

**Prompt:** 
```
Trigger a run for monitor ID 'mon_12345'.
```

**Response:** 
```
Triggering run... Monitor 'mon_12345' (Postgres Volume) has been started and is currently processing.
```

## Capabilities

### Check Data Monitor Status
List existing monitors or fetch detailed health metrics and metadata for specific monitoring points.

### Review Incident History
Pull real-time details on data quality incidents, including historical records and resolution status.

### Force Data Checks
Programmatically start a monitor run to validate data quality immediately, regardless of the schedule.

### Map Data Sources
View all connected databases, warehouses, and schemas to build a map of your company's data lineage.

### Manage Alerts
List and examine configured alert rules to ensure your team gets notified when things go wrong.

## Use Cases

### Investigating a sudden dashboard dip
A data analyst notices revenue numbers are off. Instead of filing a ticket, they ask their agent to list all monitored connections and schemas for the affected reports. This reveals that the primary Snowflake warehouse connection was listed as disconnected, pinpointing the failure point immediately.

### Pre-release data validation
A data engineer finishes an ETL pipeline and needs to confirm quality before deployment. They use their agent to `trigger_monitor_run` on the key tables, validating that row counts and null checks pass instantly, saving a manual QA cycle.

### Reviewing historical system failures
An SRE is prepping for an audit. They ask their agent to list all data incidents over the last quarter. This quickly surfaces recurring high-severity issues that might require architectural fixes, rather than just patching code.

### Auditing pipeline dependencies
A BI architect needs to know which datasets feed into a critical dashboard. They use the MCP to list all data connections and connection schemas, mapping out every dependency in minutes using simple prompts.

## Benefits

- Stop jumping between dashboards. You can list all monitors or check specific incident details directly through your AI client, keeping context in one place.
- Validate data quality on demand. Instead of waiting for a scheduled run, you use the `trigger_monitor_run` tool to manually test data integrity immediately after an ETL job.
- Understand your entire stack. The MCP lets you list all data connections and schemas, giving you full visibility into where your data comes from.
- Proactive monitoring is simple. You can list configured alerts or get details on a specific monitor to ensure the right rules are running.
- Contextualized troubleshooting. When an incident happens, you can run `list_incidents` and then drill down with `get_incident` without leaving your chat window.

## How It Works

The bottom line is that you manage complex data health checks using simple chat prompts instead of navigating multiple web dashboards.

1. Subscribe to the Metaplane MCP using your API key.
2. Connect this MCP via Vinkius to any AI client (like Cursor or Claude).
3. Ask your agent natural language questions, like 'What were the top three incidents yesterday?' and get immediate data answers.

## Frequently Asked Questions

**How do I use the Metaplane MCP to check if a specific monitor failed?**
You can first run `list_monitors` to find the monitor ID, and then use `get_monitor` for detailed status. If it failed recently, you should also check `list_incidents`.

**Can Metaplane MCP list all my databases?**
Yes. You can run the `list_data_connections` tool to enumerate every data source connected to your account.

**What is the difference between listing monitors and triggering a monitor run with Metaplane MCP?**
Listing monitors (`list_monitors`) shows you what checks are configured. Triggering a run (`trigger_monitor_run`) actually executes those checks right now to validate current data quality.

**Does the Metaplane MCP help me find which schemas exist?**
Yes, it does. You can use `list_connection_schemas` after identifying a specific connection to see all available structures within that warehouse.