Supercharge your AI with Conduit. Monitor and manage your data pipelines by asking questions.
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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.
What your AI can do
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.
Gets detailed status, timing, and error information for any specific data workflow run.
Retrieves lists of supported data source or destination connector types used in your system.
Pulls a list of every active and configured source-to-destination connection in the platform.
Retrieves the full execution history, including status and timestamps, for any data workflow.
Triggers an immediate run for a specific data integration workflow using its unique ID.
Ask an AI about this
Compatible AI Apps
OAuth 2.0 Compatible Gemini Waiting for input…
Conduit MCP: 8 Tools for Data Flow Management
These eight tools give your AI agent full control over monitoring data streams—from listing available endpoints to triggering immediate workflow runs.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Conduit on VinkiusGet 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...
List Connections
Retrieves a comprehensive list of every active connection between sources and...
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...
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.
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token from
cloud.vinkius.com. For OAuth-protected servers, expand Advanced
settings to add credentials.
Start a conversation
Open a new chat. The Conduit integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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Start with Conduit, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Conduit. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 8 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Pain of Data Infrastructure Auditing
Today, checking if your critical data pipelines are healthy is a clicking nightmare. You have to open the main dashboard, navigate to the 'Pipelines' tab, then click into individual workflows one by one. If you need to see how many runs happened last month, you jump to the 'History' section and start wrestling with filters—date range, status dropdowns, error codes. It takes time just to gather the necessary context.
With this MCP, that process disappears. You simply ask your agent: 'What was the run status for my main production pipeline last night?' The agent handles all the dashboard jumping in the background and spits out a clear answer about whether it succeeded or where it failed. It's instant, conversational diagnostics.
Getting Status with `get_run_status`
Before this MCP, checking the current status required knowing the exact workflow ID and manually looking up its metrics on a dedicated page. This was tedious, especially if multiple pipelines were running simultaneously.
Now, you pass the ID to the agent and ask for `get_run_status`. The answer is direct: 'It's Running,' or 'It failed 10 minutes ago due to X.' There’s no ambiguity about the current state of your data flow.
What your AI can actually do with this
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.
019d7579-c3f8-70bc-b156-311e85a9efad 
Here's how it actually works
The bottom line is that you talk to your data infrastructure instead of logging into a complex management dashboard.
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.
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.'
Your agent processes the request by calling the necessary internal tools and returns structured data—the current status, historical records, or detailed error reports.
Who is this actually for?
This MCP is for the technical roles who live in dashboards and deal with failed background processes. If your job involves knowing if Data A got to System B on time, you need this.
Checks continuous synchronization services between complex databases; they use it to review health metrics and audit connections.
Confirms that new pipeline deployments successfully connected endpoints after infrastructure changes, using the list available sources and destinations tools.
Requests aggregate tracking reports to validate if crucial operational data streams function continuously overnight, checking history with list_workflow_runs.
What Changes When You Connect
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.
See it in action
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.
The honest tradeoffs
Relying solely on visual dashboards
Trying to find a single run's status by clicking through the main dashboard until you hit the 'History' tab, then filtering by date, and finally selecting the ID.
Just ask your agent directly. Use list_workflow_runs or get_run_status. You get the exact data you need without any clicks.
Manually listing every endpoint type
Guessing if the platform supports S3, Kafka, or Snowflake because it's a common pattern.
Use list_available_sources and list_available_destinations. The tools tell you exactly what your system can connect to.
Confusing connections with workflows
Assuming that just because a connection exists (e.g., Source X -> Destination Y), the data moves automatically and successfully.
A connection is just a link. To confirm actual movement, you must check the workflow status using get_workflow or review the run history via list_workflow_runs.
When It Fits, When It Doesn't
Use this MCP if your primary job involves auditing data flows, checking execution logs, or validating connection health across multiple systems. It’s essential when you need to confirm if and how data moved from Point A to Point B. Don't use it if you just need to write a simple report that pulls static data; for that, a standard database query tool is better. If you only need to know what sources are available without checking any specific flow, list_available_sources works well on its own. However, never forget that this MCP helps orchestrate the process, but it doesn't replace manual data cleaning or business logic checks; those still happen outside the pipeline.
Questions you might have
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.
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