Can I trigger a build for a specific branch through the agent?

Yes! Use the `trigger_cci_pipeline` tool. You need to provide the project slug (e.g., `gh/org/repo`) and the branch name. The agent will instruct CircleCI to start the pipeline immediately.

How do I see why a specific job failed?

Use the `get_job_details` tool with the project slug and job number. Your agent will fetch the execution status and metadata, which often points to the cause of failure.

Where do I find my CircleCI API Token?

Log in to CircleCI, go to **User Settings -> Personal API Tokens**. You can generate and copy a new token from there.

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

Name: CircleCI
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 CircleCI through the 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 CircleCI "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in CircleCI?"
    )
    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 CircleCI MCP Server

Connect your CircleCI account to any AI agent and take full control of your CI/CD pipelines and software delivery through natural conversation. Streamline how you monitor and trigger automated builds.

Pydantic AI validates every CircleCI tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through the 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 recent CI/CD pipelines across your organizations natively
Trigger Management — Manually trigger new pipeline runs for specific projects and branches flawlessly
Workflow Intelligence — Access detailed information for workflows and their constituent jobs securely
Job Auditing — Retrieve detailed metadata and execution status for specific jobs flawlessly
Context Logistics — List shared environment contexts used for securing sensitive project data flawlessly
Developer Insights — Retrieve your own user profile and organization membership information directly within your workspace

The CircleCI 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 CircleCI to Pydantic AI via MCP

Follow these steps to integrate the CircleCI 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 CircleCI with type-safe schemas

Why Use Pydantic AI with the CircleCI MCP Server

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

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

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

CircleCI + Pydantic AI Use Cases

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

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

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

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

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

CircleCI MCP Tools for Pydantic AI (8)

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

get_job_details

Get detailed information for a specific job

get_my_cci_profile

Retrieve information about the authenticated user

get_workflow_details

Get detailed information for a specific workflow

list_cci_contexts

List shared contexts for an organization

list_cci_pipelines

List recent CI/CD pipelines

list_pipeline_workflows

List all workflows within a specific pipeline

list_workflow_jobs

List all jobs within a specific workflow

trigger_cci_pipeline

Trigger a new pipeline for a project

Example Prompts for CircleCI in Pydantic AI

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

"List my last 5 pipelines in CircleCI."

"Trigger a new pipeline for project 'gh/acme/api' on the 'main' branch."

"Show me the status of all jobs in workflow ID 'wf-12345'."

Troubleshooting CircleCI MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

CircleCI + Pydantic AI FAQ

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