# Conduit MCP MCP

> Conduit lets your AI agent observe and manage data integration pipelines directly through natural language chat. Instead of navigating complex web dashboards, you can ask it to check a pipeline's health status, audit specific connectors, or pull recent error logs instantly. It turns infrastructure monitoring into a simple conversation.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Tags:** data-streaming, pipeline-synchronization, real-time-data, connector-management, data-integration

## Description

The Conduit MCP connects your AI agent straight to your data synchronization layer. You don't need to log into the web dashboard to get an overview of what's happening with your critical pipelines. Instead, you talk to it. Your agent can check if active streams are running smoothly or if a connector failed hours ago, all by asking plain text questions. This lets Data Engineers and Ops teams monitor complex data movements without ever clicking through multiple tabs. If you find this MCP useful, remember that Vinkius hosts thousands of other specialized tools, letting you connect your agent to massive catalogs of services.

This ability means you can get immediate status reports on active pipelines or ask for a list of all available sources and destinations mapped across your network. It's about turning complex infrastructure auditing into a simple chat dialogue.

## Tools

### get_run_status
Checks the detailed status and error information for a single, specific workflow execution.

### get_workflow
Pulls detailed info about a data workflow, including its source, destination, and current state.

### list_connections
Retrieves a comprehensive list of every active connection between sources and destinations.

### list_available_destinations
Shows all the types of external systems that can receive your synchronized data.

### list_workflow_runs
Gathers the execution history, including status and timestamps, for a specific data workflow ID.

### list_available_sources
Lists all the kinds of external systems that can feed data into the pipeline.

### list_workflows
Provides a list of all existing data integration workflows that can be monitored or run in the platform.

### trigger_workflow
Forces a manual execution of a specific workflow using its ID.

## Prompt Examples

**Prompt:** 
```
Retrieve the current status of all major pipelines running in the production Conduit instance.
```

**Response:** 
```
I checked your pipelines successfully. Of the 8 tracked major pipelines running on the queried instance: 7 are currently marked as 'Running' with no latency spikes or recorded disruption alerts. Pipeline ID 'PG_to_Snowflake_04' is however marked 'Degraded' due to a destination connector timeout 4 minutes ago. Do you want further logs related to this degraded element specifically to review the exact recorded error response logged safely here?
```

**Prompt:** 
```
Check if there's a configured destination connector named 's3-analytics-bucket' and briefly describe its configuration parameters.
```

**Response:** 
```
Yes, I located the 's3-analytics-bucket' destination connector attached to your integration pipelines successfully. This configured destination strictly utilizes the official 'builtin:s3' plugin structure. The setup parameters authenticate via attached AWS Keys (redacted), targeting an external region of 'us-east-1', and output streams correctly in a structured JSON payload referencing an established logical path.
```

**Prompt:** 
```
Pause the pipeline 'MySQL-to-Kafka' immediately.
```

**Response:** 
```
I'm sorry, but for safety reasons, I can only inspect pipelines and display logs. I cannot pause, modify, or delete pipelines. Please use the Conduit dashboard to execute this action.
```

## Capabilities

### Check the running health of pipelines
Gets detailed status, timing, and error information for any specific data workflow run.

### Audit available endpoints
Retrieves lists of supported data source or destination connector types used in your system.

### View all connected systems
Pulls a list of every active and configured source-to-destination connection in the platform.

### Review workflow history
Retrieves the full execution history, including status and timestamps, for any data workflow.

### Manually restart a flow
Triggers an immediate run for a specific data integration workflow using its unique ID.

## Use Cases

### The nightly sync failed, and I don't know why.
Instead of manually checking the dashboard logs for the 'Sales-to-Warehouse' pipeline, you ask your agent to review its history using `list_workflow_runs`. The agent pulls up a timeline showing that while the run started fine, it failed at 3:15 AM due to an authentication error. You then use `get_workflow` to confirm the exact source and destination details needed for the fix.

### I need to validate if a new database endpoint is ready.
Before building anything, you ask your agent to list available sources using `list_available_sources`. The agent confirms that 'PostgreSQL' and 'MySQL' are both supported types. You then use `get_workflow` to see what specific parameters the system requires for those endpoints.

### We found a bug, but we need to test the fix immediately.
You don't want to wait until maintenance hours. You first list all workflows with `list_workflows` to find the correct ID, then use `trigger_workflow` to manually initiate a run for testing purposes, confirming the fix works instantly.

### We need an inventory of every data connection we have.
You ask your agent to list all connections using `list_connections`. The agent returns a clean summary showing every source and destination pair that is currently active in the platform, giving you instant visibility across the whole system.

## Benefits

- Stop digging through web dashboards. You can ask for a status overview of active or degraded pipelines using `get_run_status` or `list_workflows`, getting the answer in plain text instead.
- Need to know what systems are available? Use tools like `list_available_sources` and `list_available_destinations` to immediately see every type of endpoint supported by Conduit, without consulting documentation.
- The agent can pull a full history of runs using `list_workflow_runs`, letting you track exactly when an issue started or if the pipeline ran successfully last week.
- If something breaks, don't guess. You can ask for recent application logs or streaming output reports directly in conversation to debug errors on the fly.
- You can get a full inventory of all running pipelines by calling `list_connections`, giving you an immediate map of your entire data footprint.

## How It Works

The bottom line is that you talk to your data infrastructure instead of logging into a complex management dashboard.

1. First, append this MCP to your AI application interface and authorize the connection by providing the target instance Base URL, API Key, and Admin Password.
2. Next, ask your agent to perform a specific action using natural language. For example, 'What is the status of all major pipelines?' or 'Show me logs for yesterday's run.'
3. Your agent processes the request by calling the necessary internal tools and returns structured data—the current status, historical records, or detailed error reports.

## Frequently Asked Questions

**How do I find out what sources Conduit supports? (list_available_sources)**
You use `list_available_sources`. This tool quickly retrieves a list of every type of external data system that can feed into your pipelines, so you know what options are open to you.

**What is the difference between listing workflows and checking run history? (list_workflows vs list_workflow_runs)**
Use `list_workflows` when you need a master list of all existing data pipelines. Use `list_workflow_runs` when you want to see the execution timeline, status, and timestamps for a specific pipeline.

**Can I force a run if something breaks? (trigger_workflow)**
Yes, you can use `trigger_workflow`. You must first find the correct workflow ID using `list_workflows`, then give that ID to the agent to manually start an immediate test or fix.

**How do I check if a destination connector exists? (list_available_destinations)**
Use `list_available_destinations`. It tells you exactly what types of endpoints, like S3 buckets or data warehouses, your system can write data to.

**I need a full inventory of all my active connections; how do I use `list_connections`?**
You can get a complete list of every connected source and destination by running `list_connections`. This gives you one view of your entire data infrastructure. It shows every active pairing, so you don't have to check multiple lists to map out where all your data flows are going.

**How do I find the current operational status and timing details using `get_run_status`?**
Use `get_run_status` for an immediate health check. This tool tells you if a specific workflow is running, when it started, and if there are any errors right now. It's perfect for checking the real-time status of a major pipeline.

**If I want to know the full blueprint of a data flow, should I use `get_workflow`?**
Yes, running `get_workflow` gives you the detailed definition for that specific workflow ID. It shows exactly what source it uses, where it sends the data, and its current state. Think of it as seeing the entire design plan.

**I found an error in a pipeline; how do I retrieve the actual application logs?**
After checking status with `get_run_status`, you can then request detailed logs related to that workflow. The MCP surfaces recent application logs or streaming output reports right through conversation. This lets you debug integration failures on the fly without opening a separate dashboard.

**How do I systematically obtain an active API Key targeting the Conduit platform?**
Depending absolutely on how your infrastructure deployed the program (standalone desktop executable, core Docker containerized setups, or external Cloud instance providers), keys are defined at setup. Generally, navigate your hosted interface configurations to visually spot specific 'API section' panels or define standard keys via backend environment base configurations (for Docker setup instances, parameters typically refer natively mapping to 'CONDUIT_API_URL'). Insert keys properly downwards with other core data completely preserving original syntax precisely achieving seamless valid interactive integrations securely effortlessly resolving requirements seamlessly connecting completely natively without technical failures preventing operations running clearly correctly natively actively continuously stably.

**Can the text-based conversational integration construct entirely new data mapping pipelines logically?**
For maintaining stability and avoiding potentially flawed or disruptive integration commands inadvertently given through free text models over critical systems, this integration focuses capabilities mostly on analytical monitoring, status reviewing and component checks (observer and reporting methodologies). Direct architectural construction mapping entire data flow pipelines heavily relies on original detailed configurations inside Conduit visually rather than natural language textual generative guesses mitigating potential serious enterprise data leaks implicitly actively safely limiting functions structurally appropriately maintaining steady uncompromised safe connections.

**Which connector types can the AI list?**
The integration can list both source and destination connectors configured in your Conduit instance. Use the pipeline inspection tools to see which plugins are attached, their configuration parameters, and their current health status.