Vinkius

Dagger (Programmable CI) MCP for AI Agents. Orchestrate complex software delivery pipelines and manage container builds

Dagger (Programmable CI) lets your AI agent manage complex software delivery pipelines directly. Use this MCP to orchestrate entire build processes, pull images, execute raw GraphQL queries for testing logic, and securely handle secrets—all through natural language commands.

Dagger (Programmable CI) MCP for AI Agents MCP is compatible with Claude Claude
Dagger (Programmable CI) MCP for AI Agents MCP is compatible with ChatGPT ChatGPT
Dagger (Programmable CI) MCP for AI Agents MCP is compatible with Cursor Cursor
Dagger (Programmable CI) MCP for AI Agents MCP is compatible with Gemini Gemini
Dagger (Programmable CI) MCP for AI Agents MCP is compatible with Windsurf Windsurf
Dagger (Programmable CI) MCP for AI Agents MCP is compatible with VS Code VS Code
Dagger (Programmable CI) MCP for AI Agents MCP is compatible with JetBrains JetBrains
Dagger (Programmable CI) MCP for AI Agents MCP is compatible with Vercel Vercel
See Vinkius in Action

Give Claude and any AI agent real-world access

Execute complex build graphs

Run raw GraphQL queries against the Dagger engine to define and execute directed acyclic graph operations.

Manage container resources

Initialize scratch containers, pull images, and manage OCI-compatible states for your builds.

Query source code repositories

Connect to Git repositories to fetch the latest source code directly into your pipeline environment.

Securely handle configuration data

Create and access secrets using various sources, including environment variables or local file paths.

Inspect build state

Query the current module status or check the engine version to ensure your pipeline environment is consistent.

Waiting for input…

AI Agent
Dagger (Programmable CI) MCP for AI Agents

What AI agents can do with Dagger (Programmable CI): 10 Tools for Pipeline Orchestration

These tools give your AI agent direct access to the Dagger Engine's core functionalities, allowing it to manage containers, query Git repos, and define complex build graphs.

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 Dagger (Programmable CI) MCP

Execute Graphql Query

Run a raw GraphQL query against the Dagger engine to define complex operational graphs.

Query Cache Volume

Creates and manages cache volumes for persistent build data.

Query Container

Initializes a scratch container environment and returns its unique state ID.

Query Current Module

Retrieves detailed information about the current module's operational state.

Query Directory

Creates an empty directory within the build context and returns its ID for later use.

Query Git

Connects to a Git repository to query its current state or fetch source code.

Query Host

Retrieves details about the underlying host computing environment.

Query Http

Downloads a file from any specified URL for use in the pipeline.

Query Secret

Creates or references secrets, supporting environment variables, local files, and...

Query Version

Checks and reports the specific version number of the Dagger Engine currently...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Dagger (Programmable CI) MCP for AI Agents MCP is compatible with Claude

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

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.

3

Start a conversation

Open a new chat. The Dagger (Programmable CI) MCP for AI Agents 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
  • Create Agent Skills with progressive disclosure
  • Deploy to edge with MCPFusion framework
  • Built in DLP, auth, and compliance on each call
  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with Dagger (Programmable CI), then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,200+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Connections are secured and governed automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog weekly
Dagger (Programmable CI) MCP for AI Agents MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Dagger. 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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Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on each call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Dagger Programmable CI: Automating Complex Build Logic in DevOps

Today, building a software artifact is a mess of context switches. You write a script, run it locally, find a dependency missing, switch to the UI to upload credentials, then go back to your code to fix the pathing. It's slow, error-prone, and involves copy-pasting IDs between four different tools.

With this MCP, you tell your agent what needs to happen—'Build Service X using Git commit Y and run it in a container with secret Z.' The system handles all the steps: pulling code via `query_git`, establishing the environment (`query_container`), fetching secrets (`query_secret`), and orchestrating the entire flow through a single, reliable GraphQL definition. You get full automation without leaving your chat.

Dagger Programmable CI: Improving Container Orchestration in SRE

SREs often spend time verifying that the build environment is consistent—checking if the right base image was pulled, if the cache volume exists, or what version of the engine they are running against. This requires manually checking logs and querying multiple internal services.

Now, you can check resource integrity in a single query. By using `query_version` and `query_cache_volume`, your agent verifies that all prerequisites for deployment are met before a single line of code runs. It’s proactive validation, not reactive debugging.

What Dagger (Programmable CI) MCP for AI Agents MCP does for your AI

This MCP connects your AI client directly to the Dagger Engine, giving you programmable control over your entire CI/CD flow. Instead of jumping between dashboards or writing complex YAML files, your agent handles the orchestration. It can initialize scratch containers, pull necessary images, and query Git repositories for source code.

Need to run a specific test? Your agent executes raw GraphQL queries to compose that logic dynamically. The MCP also manages resource lifecycles—it pulls secrets securely and caches volumes so you don't repeat work. When you use Vinkius, your AI client gets access to this entire suite of tools, letting you debug pipelines or run full builds right from the chat interface.

It’s all about treating your infrastructure like another API endpoint.

Built · Hosted · Managed by Vinkius Dagger (Programmable CI) MCP for AI Agents — Container Orchestration
Server ID 019e3884-f577-717d-87af-e93ec01431b7
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Frequently asked questions about Dagger (Programmable CI) MCP for AI Agents MCP

How does Dagger (Programmable CI) MCP help me run complex deployments? +

This MCP lets you define your entire deployment process as a single, programmable workflow. Instead of writing many small steps, you use raw GraphQL queries to tell the agent exactly how all parts—containers, secrets, and source code—must interact.

Do I need to be a DevOps expert to use Dagger (Programmable CI) MCP? +

No. While it handles complex infrastructure logic, you interact with it using natural language commands via your agent. The MCP translates your conversational requests into the precise technical steps needed for a successful build.

Can Dagger (Programmable CI) MCP handle external files or URLs? +

Yes. It has tools to pull remote assets from URLs (query_http) and also manage local directory structures, allowing you to bring any needed file into the build context for testing.

Is Dagger (Programmable CI) MCP better than traditional Jenkins setups? +

It's a modern alternative. While older systems rely on rigid pipelines and configuration files, this MCP allows you to dynamically query and manage resources in real-time through your agent, offering much greater flexibility.

What if my build fails halfway through with Dagger (Programmable CI) MCP? +

The system tracks the full state. You can ask your agent to check the current module status or query the host environment to pinpoint exactly where and why the failure occurred, saving you hours of debugging.