Can my AI agent list all workspaces and drill into specific project details?

Absolutely. Use `list_workspaces` to browse all your organizational workspaces, then `list_projects` to see every project within a workspace. Your agent returns full metadata including member counts, settings, and creation dates — all without leaving your conversation.

I need to set up a new project with custom workflows and labels — can I do it all conversationally?

Yes. Tell your agent to create the project, then ask it to configure the workflow columns and create color-coded labels. The agent chains `create_project`, `list_workflows`, and `create_label` tools automatically to set up your board exactly as you describe it.

Does this integration let me modify or delete projects?

This core integration focuses on workspace exploration, project creation, and configuration management — including workflows and labels. For task-level operations like CRUD, subtasks, and checklists, use the dedicated GitScrum Tasks integration. All actions respect your GitScrum account permissions.

GitScrum MCP Server for Pydantic AI 16 tools — connect in under 2 minutes

Name: GitScrum
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 16 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect GitScrum 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

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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 GitScrum "
            "(16 tools)."
        ),
    )

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

What you can do

Browse workspaces — list all your organizational workspaces and retrieve details for each one
Manage projects — list, create, and inspect projects with full metadata including members and settings
Configure workflows — view and manage Kanban column definitions and workflow templates
Organize with labels — list, create, and update color-coded labels to categorize work
Access your profile — retrieve the authenticated user's profile across all workspaces

Pydantic AI validates every GitScrum tool response against typed schemas, catching data inconsistencies at build time. Connect 16 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.

The GitScrum MCP Server exposes 16 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 GitScrum to Pydantic AI via MCP

Follow these steps to integrate the GitScrum 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 16 tools from GitScrum with type-safe schemas

Why Use Pydantic AI with the GitScrum MCP Server

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

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

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

GitScrum + Pydantic AI Use Cases

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

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

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

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

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

GitScrum MCP Tools for Pydantic AI (16)

These 16 tools become available when you connect GitScrum to Pydantic AI via MCP:

create_project

Create a new project

create_workspace

Create a new workspace

find_project

Find a project by name

get_me

Get authenticated user profile

get_project

Get project details

get_task

Get task details by UUID

get_workspace

Get workspace details

list_labels

List labels in a project

list_project_members

List members in a project

list_projects

List projects in a workspace

list_tasks

Filter by status (todo, in-progress, done). Essential for understanding project scope and workload. List tasks in a project

list_workflows

List workflows (columns) in a project

list_workspaces

List all GitScrum workspaces

my_role

Get my role in the workspace

project_stats

Get project statistics

workspace_stats

Get workspace statistics

Example Prompts for GitScrum in Pydantic AI

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

"Show me all the workspaces I have access to on GitScrum."

"Create a new project called 'Mobile App v2' in the acme-eng workspace with a description."

"What labels are available in the web-app project?"

Troubleshooting GitScrum MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

GitScrum + Pydantic AI FAQ

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