Dagger (Programmable CI) MCP Server for Pydantic AIGive Pydantic AI 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

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Dagger (Programmable CI) through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Dagger (Programmable CI) "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Dagger (Programmable CI)?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

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.

Pydantic AI validates every Dagger (Programmable CI) tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI

When Pydantic AI 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 Pydantic AI via MCP

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

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Dagger (Programmable CI) with type-safe schemas

Why Use Pydantic AI with the Dagger (Programmable CI) MCP Server

Pydantic AI provides unique advantages when paired with Dagger (Programmable CI) through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Dagger (Programmable CI) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Dagger (Programmable CI) connection logic from agent behavior for testable, maintainable code

Dagger (Programmable CI) + Pydantic AI Use Cases

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

Type-safe data pipelines: query Dagger (Programmable CI) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Dagger (Programmable CI) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Dagger (Programmable CI) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Dagger (Programmable CI) responses and write comprehensive agent tests

Example Prompts for Dagger (Programmable CI) in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Dagger (Programmable CI) + Pydantic AI FAQ

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

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Dagger (Programmable CI) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.

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