How to Use the GitScrum Sprints MCP in Pydantic AI

Type-safe sprint automation for Pydantic AI. Validate your GitScrum Sprints data at runtime.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect GitScrum Sprints MCP to Pydantic AI

Create your Vinkius account to connect GitScrum Sprints to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup GitScrum Sprints with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-safe sprint operations in Pydantic AI

Stop worrying about malformed API responses. Every call to `create_sprint` or `update_sprint` is validated against strict models. If the data doesn't match your schema, the agent stops immediately. You get clean, predictable behavior for every sprint action.

Validate sprint KPIs with Pydantic AI

Get accurate metrics using `sprint_kpis` and `sprint_progress`. The framework ensures your agent receives the exact data types it expects. Your agent uses `sprint_stats` to generate reports that are always formatted correctly. You avoid the headache of parsing inconsistent data.

Manage backlog items in Pydantic AI

Retrieve user stories and epics with total confidence. The `list_user_stories` and `list_epics` tools provide structured output for your Pydantic models. Your agent handles `get_task` calls with ease. It ensures that every piece of task info is verified before it hits your logic.

Setup guide

Set up GitScrum Sprints MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "gitscrum-sprints-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to GitScrum Sprints tools.",
)

result = await agent.run("List recent GitScrum Sprints transactions")
print(result.output)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GitScrum. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect GitScrum Sprints now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about GitScrum Sprints MCP in Pydantic AI

Install the slim MCP package and use the MCPToolset. It provides a unified interface for your agent to interact with the server over HTTP.

It fails loudly with a validation error. This prevents your agent from acting on corrupted or unexpected data from your sprint tools.

You can. The framework is model-agnostic, so your agent can use local models or external ones to process your sprint data.

Yes, it supports both Streamable HTTP and SSE transports. You can choose the connection method that best fits your local development environment.

Your sprint data is strictly typed and validated, ensuring only valid objects are processed by your agent. This prevents unauthorized access to your task metadata.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript