Can I trigger a pipeline run for a specific branch?

Yes! Use the `trigger_codefresh_build` tool. You can provide the pipeline ID and specify the branch name in the payload. The agent will instruct Codefresh to start the build immediately.

How do I see the status of all my Kubernetes clusters in Codefresh?

Use the `list_delivery_clusters` tool. Your agent will fetch all connected clusters, showing you which targets are available for deployment and their current health status.

Where do I find my Codefresh API Key?

Log in to Codefresh, click on your profile icon, go to **User Settings**, and select **API Keys**. You can generate and copy a new token from that section.

Codefresh MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Codefresh
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 Codefresh "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Codefresh MCP Server

Connect your Codefresh account to any AI agent and take full control of your CI/CD and cloud-native delivery through natural conversation. Streamline how you automate and monitor software deployments natively.

Pydantic AI validates every Codefresh tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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

Pipeline Oversight — List and retrieve details for all CI/CD pipelines including their configurations natively
Build Management — Trigger new builds for specific pipelines and specify branches or variables flawlessly
Workflow Intelligence — Access detailed status and execution info for recent builds (workflows) flawlessly
Cluster Logistics — Monitor all connected Kubernetes and delivery clusters to verify deployment targets securely
Environment Auditing — List shared contexts, including secrets and variables, used in your workflows securely
integrated Visibility — Retrieve detailed build metadata and user profile information directly within your workspace

The Codefresh MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Codefresh to Pydantic AI via MCP

Follow these steps to integrate the Codefresh MCP Server with Pydantic AI.

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 8 tools from Codefresh with type-safe schemas

Why Use Pydantic AI with the Codefresh MCP Server

Pydantic AI provides unique advantages when paired with Codefresh 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 Codefresh integration code

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

Dependency injection system cleanly separates your Codefresh connection logic from agent behavior for testable, maintainable code

Codefresh + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Codefresh MCP Server delivers measurable value.

Type-safe data pipelines: query Codefresh with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Codefresh tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Codefresh and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Codefresh responses and write comprehensive agent tests

Codefresh MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Codefresh to Pydantic AI via MCP:

get_build_execution_details

Get detailed status and execution info for a specific build

get_my_codefresh_profile

Retrieve information about the authenticated user and account

get_pipeline_configuration

Get detailed information for a specific pipeline

list_codefresh_builds

List all recent builds (workflows) in the account

list_codefresh_pipelines

List all CI/CD pipelines in the account

list_delivery_clusters

List all connected Kubernetes/Delivery clusters

list_shared_contexts

List all shared environment contexts (secrets, variables)

trigger_codefresh_build

Trigger a new build for a specific pipeline

Example Prompts for Codefresh in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Codefresh immediately.

"List all my Codefresh pipelines."

"Trigger the 'api-service-ci' pipeline on the 'develop' branch."

"Show me the status of my recent builds."

Troubleshooting Codefresh MCP Server with Pydantic AI

Common issues when connecting Codefresh to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Codefresh + Pydantic AI FAQ

Common questions about integrating Codefresh 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 Codefresh MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.