Can I check the status of my latest Bitbucket Pipeline?

Yes! Use the `list_pipelines` tool with your Workspace and Repository slug. Your agent will fetch the latest build statuses, showing if they passed or failed.

How do I list all open pull requests for a repository?

Simply ask the agent to `list_pull_requests` and provide the Workspace and Repository slug. It will retrieve all current PRs awaiting review.

Does the integration allow merging code directly?

Currently, the toolset is focused on querying and monitoring (Read-Only). You can list and inspect PRs, commits, and branches, but destructive actions like merging or deleting branches must be done through the Bitbucket UI for safety.

Bitbucket MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Bitbucket
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Bitbucket 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 Bitbucket "
            "(10 tools)."
        ),
    )

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

Connect your Bitbucket Cloud account to any AI agent and orchestrate your software development workflows through natural conversation.

Pydantic AI validates every Bitbucket tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Repository Oversight — List all repositories within your workspaces and retrieve detailed metadata.
Pull Request Management — Query and inspect pull requests to monitor code reviews and merge statuses.
Commit & Branch Discovery — List the latest commits and active branches across your projects.
CI/CD Monitoring — Retrieve the status of Bitbucket Pipelines to ensure successful builds.
Issue Tracking — List and retrieve issues for repositories with enabled trackers.
Workspace Coordination — Access and manage your team's workspaces and user profiles.

The Bitbucket MCP Server exposes 10 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 Bitbucket to Pydantic AI via MCP

Follow these steps to integrate the Bitbucket 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 10 tools from Bitbucket with type-safe schemas

Why Use Pydantic AI with the Bitbucket MCP Server

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

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

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

Bitbucket + Pydantic AI Use Cases

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

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

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

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

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

Bitbucket MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Bitbucket to Pydantic AI via MCP:

get_pull_request

Get details of a specific pull request

get_repository

Get details of a specific repository

get_user_profile

Get authenticated user profile

list_branches

List branches for a repository

list_commits

List commits for a repository

list_issues

List issues for a repository (if tracker is enabled)

list_pipelines

List CI/CD pipelines for a repository

list_pull_requests

List pull requests for a repository

list_repositories

List repositories in a workspace

list_workspaces

List all accessible workspaces

Example Prompts for Bitbucket in Pydantic AI

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

"List all pull requests in repository 'my-app' within workspace 'my-team'."

"Check the status of the last pipeline run for 'my-app'."

"List the last 5 commits in repository 'my-app'."

Troubleshooting Bitbucket MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Bitbucket + Pydantic AI FAQ

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