Can my AI agent find a specific task by its human-readable code like WEB-42?

Yes! Use the `get_task_by_code` tool with the task code (e.g., WEB-42). Your agent will return the full task details including description, assignees, workflow status, labels, sprint association, time estimates, and checklist progress — in seconds.

I want to quickly see what I need to work on today. Can the agent help?

Absolutely. Use `my_today_tasks` to see everything due today, or `my_tasks` for your full assignment list across all workspaces. The agent returns task titles, project context, due dates, and current workflow status — your daily standup briefing in one command.

Can the agent manage task assignments and checklists too?

Yes. Use `assign_member` and `unassign_member` to manage task assignments by username. For checklists, use `create_checklist_item` to add items (including nested sub-items via parent_id), and `toggle_checklist_item` to mark them done. The complete task lifecycle is covered — from creation to completion.

GitScrum Tasks MCP Server for Pydantic AI 28 tools — connect in under 2 minutes

Name: GitScrum Tasks
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 28 Tools SDK

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

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

What you can do

Full task lifecycle — create, update, delete, and toggle completion on tasks with rich metadata including types, effort levels, and dates
Advanced filtering — query tasks by status, sprint, user story, assignee, label, type, effort, workflow column, blocker flag, and date ranges
Subtask management — list, link, unlink subtasks and discover related tasks across your project
Checklists — add checklist items with sub-items and toggle completion for granular progress tracking
Team coordination — assign and unassign members, duplicate tasks, move between projects, and set story points
Comments — list, create, update, and delete task comments for rich collaboration context

Pydantic AI validates every GitScrum Tasks tool response against typed schemas, catching data inconsistencies at build time. Connect 28 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 Tasks MCP Server exposes 28 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 Tasks to Pydantic AI via MCP

Follow these steps to integrate the GitScrum Tasks 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 28 tools from GitScrum Tasks with type-safe schemas

Why Use Pydantic AI with the GitScrum Tasks MCP Server

Pydantic AI provides unique advantages when paired with GitScrum Tasks 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 Tasks 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 Tasks connection logic from agent behavior for testable, maintainable code

GitScrum Tasks + Pydantic AI Use Cases

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

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

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

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

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

GitScrum Tasks MCP Tools for Pydantic AI (28)

These 28 tools become available when you connect GitScrum Tasks to Pydantic AI via MCP:

assign_member

Assign a user to a task

create_checklist_item

Use parent_id to create sub-items. Add a checklist item to a task

create_comment

Supports rich text content. Add a comment to a task

create_task

Create a new task

create_task_type

g., Chore, Tech Debt) with a hex color code. Create a new task type

delete_comment

Delete a comment

delete_task

This action cannot be undone. Delete a task permanently

duplicate_task

Duplicate a task

get_task

Get task details by UUID

get_task_by_code

g., WEB-42) instead of UUID. Get task by human-readable code

link_subtask

Link an existing task as a subtask

list_checklists

List checklists on a task

list_comments

Comments support rich text. List comments on a task

list_effort_levels

List effort/priority levels

list_subtasks

List subtasks of a task

list_task_types

) with their colors. List task types in a project

list_tasks

Filter by status (todo, in-progress, done), sprint, user_story, users, labels, type, effort, workflow, is_blocker, is_archived, unassigned, created_at (YYYY-MM-DD=YYYY-MM-DD), closed_at, per_page. List tasks with advanced filters

move_task_to_project

Move a task to a different project

my_tasks

Get all tasks assigned to me

my_today_tasks

Get tasks due today

related_tasks

Get tasks related to a task

set_task_estimate

Set story points / estimate for a task

toggle_checklist_item

Toggle a checklist item done/undone

toggle_task_done

Toggle task completion status

unassign_member

Remove a user from a task

unlink_subtask

Unlink a subtask

update_comment

Edit an existing comment

update_task

Update an existing task

Example Prompts for GitScrum Tasks in Pydantic AI

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

"Show me all in-progress tasks in the web-app project."

"Create a bug task 'Login timeout on slow connections' in web-app and assign it to janedoe."

"Add a checklist to task WEB-42 with items for 'Write unit tests', 'Update docs', and 'Deploy to staging'."

Troubleshooting GitScrum Tasks MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

GitScrum Tasks + Pydantic AI FAQ

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