Argo Workflows Connector for AI agents.
6 live capabilities
Manage Kubernetes orchestrations and debug pipeline failures in real-time.
Waiting for input…
Why people use Argo Workflows
Debugging Argo Workflows Pipelines with Argo Workflows
This Connector changes that by letting you just ask. You can tell your agent to find the failed job and show you the resource tree immediately. It pulls the data from your cluster and puts it right in your chat, so you spend less time clicking and more time fixing.
What Vinkius changes
You get a conversational interface for your Kubernetes orchestration without leaving your chat window.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Debugging a stalled ETL
A data engineer asks the agent to check why the nightly load is stuck.
- Real-world use case 02
Auditing production templates
A DevOps lead wants to see what reusable components are available.
- Real-world use case 03
Checking recurring jobs
An SRE wants to make sure the daily backups are scheduled.
Complete set · 6capabilities
The complete Argo Workflows capability set.
These are the exact actions your AI can choose when you ask it to work with Argo Workflows.
01—03
3 capabilities in this set.
Part of 6 available through Argo Workflows.
- 01 Capability
List workflows
List all workflows in a specific Kubernetes namespace. It helps you see what's currently running or pending.
- 02 Capability
Get workflow
Get the detailed resource tree and status for a single Argo workflow. Use this to find the exact node where a process failed.
- 03 Capability
List archived workflows
List archived workflows from your Argo history. Use this to search for past infrastructure patterns.
04—06
3 capabilities in this set.
Part of 6 available through Argo Workflows.
- 04 Capability
List workflow templates
List all workflow templates defined in a namespace. This lets you see your reusable components at a glance.
- 05 Capability
List cron workflows
List scheduled cron workflows in a namespace. It's the easiest way to check your recurring jobs.
- 06 Capability
Get server info
Get general information about your Argo Workflows instance. Use this to verify your connection and cluster status.
Set up in minutes
One URL. Then ask Argo Workflows to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Argo Workflows from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_XgODuKGP8hKPvAVimTmrdsOr5ag915WZ6oPDkNoq/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Argo Workflows, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Argo Workflows for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_XgODuKGP8hKPvAVimTmrdsOr5ag915WZ6oPDkNoq/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Argo Workflows URL.
- Step 03
Save and start
Save the connection and enable Argo Workflows in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"argo-workflows": {
"url": "https://edge.vinkius.com/vk_preview_XgODuKGP8hKPvAVimTmrdsOr5ag915WZ6oPDkNoq/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Argo Workflows
Open Agent mode in chat and ask: "Using Argo Workflows, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"argo-workflows": {
"url": "https://edge.vinkius.com/vk_preview_XgODuKGP8hKPvAVimTmrdsOr5ag915WZ6oPDkNoq/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Argo Workflows
Ask Copilot: "Using Argo Workflows, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"argo-workflows": {
"url": "https://edge.vinkius.com/vk_preview_XgODuKGP8hKPvAVimTmrdsOr5ag915WZ6oPDkNoq/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Argo Workflows
Open Cascade and ask: "Using Argo Workflows, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"argo-workflows": {
"url": "https://edge.vinkius.com/vk_preview_XgODuKGP8hKPvAVimTmrdsOr5ag915WZ6oPDkNoq/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Argo Workflows
Ask Cline: "Using Argo Workflows, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add argo-workflows --transport http "https://edge.vinkius.com/vk_preview_XgODuKGP8hKPvAVimTmrdsOr5ag915WZ6oPDkNoq/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Argo Workflows
Ask Claude: "Using Argo Workflows, show me...". 6 tools are ready
Where the request belongs
Work Argo Workflows can move forward.
This is for the engineer who's tired of manually refreshing dashboards at 2am to see if a nightly ETL job finished or why a production pipeline is hanging.
DevOps Engineer
Debugs pipeline failures and audits running jobs during incidents.
Data Engineer
Monitors complex ETL workflows and scheduled cron operations.
SRE
Quickly queries the health of the Argo instance and retrieves historical metrics.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Equip your AI to manage Kubernetes environments directly through Rancher, overseeing clusters, namespaces, and active pods.
Qovery
Bring effortless Kubernetes and cloud deployment management to your IDE. List environments, check application states, restart pods, and deploy exact Git commits via chat.
Porter PaaS
Orchestrate Kubernetes clusters via Porter. manage apps, projects, container tags, and enforce rollouts directly with your AI.
Portainer
Manage Docker containers and environments via Portainer. list, create, and start containers directly from your AI agent.
HashiCorp Nomad
Manage workloads and orchestration via Nomad. track jobs, nodes, and deployments directly from your AI agent.
Bring your own AI
Change the model, client or framework. Keep Argo Workflows connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Argo Workflows.
The practical details behind the request, access and result.
What can I do with the Argo Workflows MCP?
You can use it to monitor, list, and inspect your Kubernetes orchestrations. It lets you see what's running, check your scheduled jobs, and find specific pods that might be causing issues.
Can I use this to see why my Kubernetes pipeline failed?
Yes. By asking your agent to inspect a specific workflow, it can pull the resource tree and show you exactly which node failed and why.
How do I check my scheduled cron jobs?
You can simply ask your agent to list your cron workflows. It will pull the list of all scheduled recurring tasks from your namespace.
Can the Argo Workflows MCP see my archived jobs?
Yes, it can pull up archived workflows from your history, which is helpful for auditing past performance or finding old error patterns.
Does this work with any AI client like Claude or Cursor?
Yes, it works with any MCP-compatible client, including Claude, Cursor, and Windsurf, giving you a consistent experience across your capabilities.
Can I see my workflow templates using this Connector?
Yes. You can ask your agent to list the templates in a namespace to see all your reusable, parameterized components.
How do I connect my Argo cluster to my AI agent?
You just need to provide your Argo Cluster URL and your RBAC Bearer Token in your Connector settings. From there, your agent can query your cluster directly.
Can my AI agent figure out exactly which pod/node failed in an active workflow execution?
Yes. If a workflow fails, you can ask your agent to retrieve the workflow tree by name. The agent uses the get_workflow capability to inspect the deeply nested structure, traverse the active nodes, and pinpoint the exact step or container that resulted in an error state without you ever needing to click through the Argo UI.
Can I list only scheduled periodic jobs across my cluster?
Absolutely. You can use the dedicated list_cron_workflows capability to isolate and return strictly workloads orchestrated on a time schedule across any namespace, saving you from parsing through thousands of isolated runs.
Do I need to expose my internal Kubernetes API to use this?
No. The integration strictly interfaces with the Argo Server UI/API layer via standard REST traffic using a scoped ServiceAccount Bearer token. Your cluster's overarching master kube-apiserver remains safely isolated from external agentic logic.
One connection away
Give your agent a direct line to Argo Workflows.
Connect Argo Workflows once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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