Dagger (Programmable CI) MCP Server for CrewAIGive CrewAI instant access to 10 tools to Execute Graphql Query, Query Cache Volume, Query Container, and more

Name: Dagger (Programmable CI)
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Connect your CrewAI agents to Dagger (Programmable CI) through Vinkius, pass the Edge URL in the `mcps` parameter and every Dagger (Programmable CI) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The Dagger (Programmable CI) MCP Server for CrewAI is a standout in the Loved By Devs category — giving your AI agent 10 tools to work with, ready to go from day one.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Dagger (Programmable CI) Specialist",
    goal="Help users interact with Dagger (Programmable CI) effectively",
    backstory=(
        "You are an expert at leveraging Dagger (Programmable CI) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Dagger (Programmable CI) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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About Dagger (Programmable CI) MCP Server

Connect to the Dagger Engine to orchestrate your delivery pipelines using a powerful, programmable GraphQL API. This server allows your AI agent to interact directly with Dagger's Directed Acyclic Graph (DAG) of operations.

When paired with CrewAI, Dagger (Programmable CI) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Dagger (Programmable CI) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Container Orchestration — Initialize scratch containers, pull images, and manage OCI-compatible states.
GraphQL Workflows — Execute raw GraphQL queries to compose complex build and test logic dynamically.
Source Control — Query Git repositories and host environments to pull source code into your pipelines.
Resource Management — Handle secrets securely, manage persistent cache volumes, and fetch remote files via HTTP.
Module Inspection — Query the current module state and engine version to ensure environment consistency.

The Dagger (Programmable CI) MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 10 Dagger (Programmable CI) tools available for CrewAI

When CrewAI connects to Dagger (Programmable CI) through Vinkius, your AI agent gets direct access to every tool listed below — spanning ci-cd, container-orchestration, pipeline-automation, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

execute

Execute graphql query on Dagger (Programmable CI)

You can chain fields to create a Directed Acyclic Graph (DAG) of operations. Execute a raw GraphQL query against the Dagger engine

query

Query cache volume on Dagger (Programmable CI)

Constructs a cache volume

query

Query container on Dagger (Programmable CI)

Creates a scratch container and returns its ID

query

Query current module on Dagger (Programmable CI)

Queries the current module

query

Query directory on Dagger (Programmable CI)

Creates an empty directory and returns its ID

query

Query git on Dagger (Programmable CI)

Queries a Git repository

query

Query host on Dagger (Programmable CI)

Queries the host environment

query

Query http on Dagger (Programmable CI)

Returns a file from a URL

query

Query secret on Dagger (Programmable CI)

g., env://VAR_NAME, file://PATH, cmd://COMMAND). Creates a new secret

query

Query version on Dagger (Programmable CI)

Get the Dagger Engine version

Connect Dagger (Programmable CI) to CrewAI via MCP

Follow these steps to wire Dagger (Programmable CI) into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Dagger (Programmable CI)

Why Use CrewAI with the Dagger (Programmable CI) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Dagger (Programmable CI) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Dagger (Programmable CI) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Dagger (Programmable CI) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Dagger (Programmable CI) for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Dagger (Programmable CI), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Dagger (Programmable CI) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Dagger (Programmable CI) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Dagger (Programmable CI) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Dagger (Programmable CI) immediately.

"Check the current version of the Dagger engine."

"Initialize a scratch container and return its ID."

"Get the state of the git repository at https://github.com/dagger/dagger."

Troubleshooting Dagger (Programmable CI) MCP Server with CrewAI

Common issues when connecting Dagger (Programmable CI) to CrewAI through Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Dagger (Programmable CI) + CrewAI FAQ

Common questions about integrating Dagger (Programmable CI) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.