Can my AI agent show me the burndown chart data for the current sprint?

Yes! Use `sprint_reports` with the resource set to 'burndown'. Your agent returns the ideal versus actual burn-down data points, so you can visualize or analyze sprint health instantly. You can also request 'burnup', 'performance', or 'member_distribution' reports.

Can I see what tasks are in a specific sprint?

Absolutely. Use `list_tasks` with the `sprint_slug` filter to see only tasks belonging to that sprint. You can further filter by status (todo, in-progress, done) to focus on what matters. Then use `get_task` to drill into any specific task for full details.

Does this integration support sprint velocity and metrics tracking?

Yes. Use `sprint_metrics` for detailed velocity, throughput, and efficiency data. Combined with `sprint_kpis` for high-level indicators and `sprint_stats` for task distribution analysis, you get a complete performance picture across any sprint.

GitScrum Sprints MCP Server for Pydantic AI 15 tools — connect in under 2 minutes

Name: GitScrum Sprints
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 15 Tools SDK

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

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

What you can do

Sprint lifecycle — create, update, delete, and inspect sprints with precise date ranges and configurations
Performance analytics — access sprint KPIs, detailed statistics, progress tracking, and velocity metrics in real-time
Visual reports — retrieve burndown, burnup, performance, and distribution chart data for any sprint
Backlog management — list and create user stories, browse epics, and view tasks filtered by sprint
Cross-workspace visibility — list sprints across all workspaces for portfolio-level oversight

Pydantic AI validates every GitScrum Sprints tool response against typed schemas, catching data inconsistencies at build time. Connect 15 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 Sprints MCP Server exposes 15 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 Sprints to Pydantic AI via MCP

Follow these steps to integrate the GitScrum Sprints 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 15 tools from GitScrum Sprints with type-safe schemas

Why Use Pydantic AI with the GitScrum Sprints MCP Server

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

GitScrum Sprints + Pydantic AI Use Cases

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

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

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

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

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

GitScrum Sprints MCP Tools for Pydantic AI (15)

These 15 tools become available when you connect GitScrum Sprints to Pydantic AI via MCP:

all_sprints

List sprints across all workspaces

create_sprint

Create a new sprint

create_user_story

Create a user story

get_sprint

Get sprint details

get_task

Get task details by UUID

list_epics

List epics in a project

list_sprints

List sprints in a project

list_tasks

Use the sprint_slug filter to see only tasks belonging to a specific sprint. Filter by status (todo, in-progress, done). List tasks in a project, optionally filtered by sprint

list_user_stories

List user stories in a project

sprint_kpis

Get sprint KPIs

sprint_metrics

Get detailed sprint metrics

sprint_progress

Get current sprint progress

sprint_reports

Resource: burndown, burnup, performance, types, efforts, member_distribution, task, type_distribution. Get sprint reports with charts

sprint_stats

Get sprint statistics

update_sprint

Update an existing sprint

Example Prompts for GitScrum Sprints in Pydantic AI

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

"What's the progress of our current sprint in the web-app project?"

"Create a new sprint 'Sprint 15 — Payments' from April 14 to April 28."

"Show me the velocity metrics for the last completed sprint."

Troubleshooting GitScrum Sprints MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

GitScrum Sprints + Pydantic AI FAQ

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