# Dagger MCP for AI Agents AI Agent Connect

> Dagger MCP lets your AI agent run and manage build pipelines using Dagger's programmable engine. Instead of manually scripting complex CI steps, you can have your agent execute GraphQL queries to handle container orchestration, pull source code, and manage secrets. It turns your CI/CD workflow into a programmable DAG that your agent can interact with directly.

## Overview
- **Category:** loved-by-devs
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_jU749naeDKubVrRR38BTiJPB8XzxtT6YXppJtFAW/ai-agent-connect
- **Tags:** ci-cd, container-orchestration, pipeline-automation, graphql, infrastructure-as-code, build-automation

## Description

Dagger lets you treat your CI/CD pipeline like code. This Connector connects your AI client to the Dagger Engine, giving it the ability to build, test, and deploy applications through a programmable GraphQL API. Instead of the AI guessing how to run a build, it interacts with a Directed Acyclic Graph (DAG) of operations to ensure every step happens in the right order. You can have your agent initialize containers, pull specific git repositories, or fetch remote files without you having to jump between terminal windows and configuration files. It handles the heavy lifting of OCI-compatible states and persistent cache volumes while keeping secrets secure. By using this Connector within the Vinkius catalog, you bridge the gap between your high-level instructions and the low-level execution of complex infrastructure tasks. It is about giving your agent the same control a DevOps engineer has when using the Dagger CLI, but through a natural conversation.

## Tools

### execute_graphql_query
Run raw GraphQL queries to chain fields into a Directed Acyclic Graph of operations. This lets your agent build complex logic dynamically.

### query_cache_volume
Create a cache volume to persist data between different build steps. It helps avoid redundant work in your pipeline.

### query_container
Initialize a new scratch container and get its unique state ID. Use this to start a clean environment for your tasks.

### query_current_module
Check the current module state to ensure the environment is consistent. This helps your agent verify the setup before running commands.

### query_directory
Create a new empty directory and retrieve its ID for use in your pipeline. This is useful for organizing files during a build.

### query_host
Query the host environment to get information about the underlying system. Use this to check your local or remote machine status.

### query_http
Fetch a file from a URL to include it in your build process. This lets your agent grab remote configs or assets easily.

### query_secret
Create a new secret using environment variables, files, or commands. It keeps your credentials safe while your agent runs builds.

### query_version
Get the current Dagger Engine version to verify your setup. Use this to make sure your environment matches your requirements.

### query_git
Query a specific Git repository to pull source code into your workflow. Your agent can fetch the latest code automatically.

## Prompt Examples

**Prompt:** 
```
Check the Dagger engine version.
```

**Response:** 
```
I'll check that for you. The local Dagger engine is running version v0.12.5.
```

**Prompt:** 
```
Initialize a scratch container and return its ID.
```

**Response:** 
```
I've initialized a new scratch container. The state ID is `cont-abc123xyz`. You can now use this ID to chain further operations.
```

**Prompt:** 
```
Get the state of the git repository at https://github.com/dagger/dagger.
```

**Response:** 
```
Querying the repository... I've retrieved the state for the Dagger repository. The directory ID is `dir-repo789`. Would you like to list its contents?
```

## Capabilities

### Execute complex build logic
Run multi-step operations by chaining fields into a Directed Acyclic Graph.

### Spin up scratch containers
Create isolated environments for clean and reproducible task execution.

### Pull source code from Git
Fetch specific repositories into your pipeline automatically.

### Fetch remote files
Retrieve assets or configurations from URLs during the build process.

### Manage persistent caches
Construct volumes to save data between different build steps.

### Query module states
Verify the current environment and engine version for consistency.

## Use Cases

### Debugging a failing build
A developer asks the agent to check the current module state and run a specific GraphQL query to see why a container step failed.

### Dynamic CI generation
A DevOps engineer tells the agent to pull a repo, create a directory, and fetch a remote config file to build a custom image.

### Secure secret management
An SRE asks the agent to create a secret from an environment variable and then run a build that requires that specific credential.

### Cache optimization
A developer notices slow builds and asks the agent to construct a cache volume to persist build artifacts across runs.

## Benefits

- Stop manual YAML debugging by letting your agent build complex logic dynamically using GraphQL queries.
- Speed up your builds by having your agent manage persistent caches to avoid redundant work across steps.
- Maintain clean environments by letting your agent spin up scratch containers for every isolated task.
- Automate source fetching by letting your agent pull specific repositories into your pipeline on demand.
- Keep your credentials safe by letting your agent handle secrets from environment variables or files.
- Ensure environment consistency by having your agent check your current module state and engine version.

## How It Works

The bottom line is it gives your agent a direct line to execute programmable CI pipelines.

1. Start your Dagger Engine locally or in a remote environment.
2. Provide your AI client with the session port and token from your Dagger CLI.
3. Ask your agent to run builds or manage containers using natural language.

## Frequently Asked Questions

**How does Dagger MCP help with CI/CD?**
It gives your AI agent the ability to run and manage programmable CI pipelines. Instead of writing long scripts, your agent can execute complex build logic and manage container states directly.

**Can I use Dagger MCP to manage secrets?**
Yes, your agent can create and manage secrets from environment variables, files, or commands. This keeps your sensitive credentials out of the chat history while still allowing the build to run.

**Does Dagger MCP work with my existing Dagger Engine?**
Yes, it connects directly to your running Dagger Engine. You just need to provide the session port and token generated by your Dagger CLI.

**How does Dagger MCP handle container states?**
It allows your agent to initialize scratch containers and retrieve unique state IDs. This makes it easy to chain multiple operations together in a specific order.

**Can Dagger MCP pull code from different repos?**
Yes, your agent can query specific Git repositories to pull source code into your pipeline automatically, making it easy to build projects with multiple dependencies.

**Is Dagger MCP good for debugging complex pipelines?**
It is excellent for debugging because it allows your agent to inspect the current module state and run specific GraphQL queries to isolate exactly where a build step is failing.

**How do I run a specific build command using Dagger?**
You can use the `execute_graphql_query` tool to send a raw GraphQL query to the Dagger engine, allowing you to chain container operations like `from`, `withExec`, and `stdout`.

**Can I access files from my local machine in the pipeline?**
Yes, the `query_host` tool allows you to retrieve the state ID for your host environment, which can then be used to mount local directories into your Dagger containers.

**How are secrets handled in this integration?**
Use the `query_secret` tool to load secrets from URIs (like `env://` or `file://`). This returns a secret ID that can be safely passed to other Dagger operations without exposing the plaintext value.