# Conduit MCP for AI Agents AI Agent Connect > Conduit lets you manage and monitor your data integration pipelines through an AI agent. It gives your agent the ability to check streaming health, audit source and destination connectors, and pull logs for debugging. Instead of jumping between tabs, you can just ask your agent to see if a specific data sync is running or why a certain workflow failed. ## Overview - **Category:** developer-tools - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_BeW6ubE4lh69UAetvEDl1Ynez7lChM15fiyrtFlY/ai-agent-connect - **Tags:** data-streaming, pipeline-synchronization, real-time-data, connector-management, data-integration ## Description Conduit lets you manage and monitor your data integration pipelines through an AI agent. Managing complex data pipelines usually feels like a constant game of whack-a-mole. You're stuck jumping between different dashboards to see if a sync actually finished, why a specific connector is hanging, or what the latest error message says in a sea of logs. Conduit changes that by letting you treat your data infrastructure as a conversational interface. Instead of manually clicking through a web UI to find a specific workflow, you can just ask your agent to tell you what's happening. You get immediate answers on whether your production streams are healthy or if a new deployment is actually hitting the right endpoints. Because this Connector is hosted on the Vinkius catalog, you can plug it into your existing setup and give your agent the eyes it needs to oversee your entire synchronization layer. It turns manual monitoring into a proactive conversation, making it way easier to catch issues before they cause downtime. You can quickly swap between checking source types, auditing active connections, or pulling specific run histories without ever leaving your chat window. It's about getting the status you need without the friction of navigating a complex dashboard. This setup is especially helpful when you need to verify that multiple data points are moving correctly across different environments, ensuring your data remains consistent and your operations stay on track without the usual headache of manual verification. ## Tools ### get_run_status Get the current status, timing, and error details for a specific workflow run to see where a sync stalled. ### get_workflow Retrieve the source, destination, and current status of a specific workflow to verify your data is moving correctly. ### list_connections See a complete list of all active source and destination connections to audit your entire infrastructure in one glance. ### list_available_destinations See all the different data destination connector types supported by Conduit to explore where you can send your data next. ### list_workflow_runs View the execution history, including status and timestamps, for a specific workflow to track pipeline performance. ### list_available_sources See all the different data source connector types supported by Conduit to quickly check your options for new data inputs. ### list_workflows Get a list of all data integration workflows to find specific IDs for managing your other operations. ### trigger_workflow Manually start a new run for a specific workflow after identifying its ID to kick off data syncs on demand. ## Prompt Examples **Prompt:** ``` Are any of my data pipelines currently showing errors? ``` **Response:** ``` I've checked your active pipelines. Most are running fine, but the 'Inventory_Sync' pipeline is currently marked as **Degraded**. * **Status:** Degraded * **Issue:** Destination connector timeout * **Time:** 3 minutes ago Would you like me to pull the specific logs for that run? ``` **Prompt:** ``` Show me all the data sources I can connect to. ``` **Response:** ``` You can connect to several types of sources through Conduit. Here are the ones currently supported: * **PostgreSQL** * **MySQL** * **MongoDB** * **S3 Buckets** * **Google BigQuery** Would you like to see the specific destination options as well? ``` **Prompt:** ``` Check the status of the 'Marketing_Data' workflow. ``` **Response:** ``` The 'Marketing_Data' workflow is currently **Running**. * **Started:** 5 minutes ago * **Progress:** 4,200 records processed * **Estimated Completion:** 2 minutes Everything looks on track. ``` ## Capabilities ### Check workflow run status Get the current status, timing, and error details for a specific data workflow run. ### View workflow details Retrieve the source, destination, and current status of a specific workflow. ### Audit active connections See a complete list of all active source and destination connections in your setup. ### Explore destination connectors See all the different data destination connector types supported by Conduit. ### Track execution history View the execution history, including status and timestamps, for a specific workflow. ### Explore source connectors See all the different data source connector types supported by Conduit. ### List all workflows Get a list of all data integration workflows to find specific IDs for management. ### Manually trigger runs Manually start a new run for a specific workflow to kick off data syncs on demand. ## Use Cases ### Checking production health An engineer asks for the status of all major pipelines and gets a summary of which ones are running or degraded. ### Connector auditing A user wants to know if a specific S3 bucket is actually connected and what its configuration looks like. ### Deployment verification A DevOps pro asks the agent to confirm if a new pipeline successfully connected its endpoints after a migration. ### Overnight monitoring A sysadmin asks for a report on all workflows that ran last night to ensure data stayed in sync. ## Benefits - Stop manual dashboard hopping by using list_workflows to see your entire pipeline overview in one chat. - Quickly identify failing syncs with get_run_status, which pulls timing and error info instantly. - Audit your infrastructure by using list_connections to verify every active source and destination. - Debug errors on the fly by asking your agent to pull logs for specific runs using list_workflow_runs. - Verify your setup is correct by using get_workflow to check source and destination mappings. - Explore your options for new integrations using list_available_sources and list_available_destinations. ## How It Works The bottom line is you get instant visibility into your data pipelines without having to navigate a web dashboard. 1. Add the Conduit MCP to your AI client and provide your Base URL, API Key, and Admin Password. 2. Describe the specific pipeline, connector, or log history you want to check in plain English. 3. Receive a summary of the health, logs, or connection details directly in your chat window. ## Frequently Asked Questions **Can I use the Conduit MCP to see if my data syncs are working?** Yes, you can use it to check the real-time status of your pipelines. Your agent will tell you if they're running, paused, or degraded. **How does the Conduit MCP help with debugging data errors?** It allows your agent to fetch specific run histories and logs. This means you can see the exact error message without hunting through a complex dashboard. **Can I use Conduit to see what data sources are supported?** Yes, you can ask your agent to list all available source and destination connector types supported by the platform. **Can I manually start a data pipeline with the Conduit MCP?** You can trigger a specific workflow run manually. Just ask your agent to start the one you need, and it will handle the request. **Does the Conduit MCP let me see my active connections?** Yes, it can retrieve a full list of all your active source and destination connections to help you audit your setup. **Is the Conduit MCP good for monitoring production data streams?** It's perfect for that. It gives you a clear view of your streaming health and allows you to check for any latency or disruption alerts instantly. **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.