πŸ“š Learn how to turn Haystack pipelines into production-ready REST APIs or MCP tools

🌸 Spring into Haystack 🌸

πŸ’™ Thank you for your interest in Spring into Haystack!

Submissions are now closed, but don’t let that stop you!

You’re still welcome to follow the instructions below and build your own Haystack AI Agent that integrates with MCP servers.

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See you at the next event! πŸ‘‹

🌱 Sprout an Agent with Haystack & MCP

Spring has sprung β€” flowers are blooming, birds are chirping, and it’s the perfect time for some fresh ideas to take root πŸ₯

Spring cleaning can wait… you’re here to level up your AI dev skills with Haystack and take part in a blossoming new movement: Anthropic’s Model Context Protocol (MCP) 🌐

This challenge invites you to sprout a Haystack Agent that speaks MCP and connects to external systems β€” starting with GitHub.


🧠 What’s MCP?

The Model Context Protocol is an emerging open standard that unlocks two-way, secure, and structured communication between your AI applications and the tools they use.

In this model:

  • MCP Servers act like translators between your app and real-world tools β€” whether it’s API like GitHub, a database, or a local file system. Think of them as smart wrappers that expose APIs and systems to AI in a consistent, reliable way.
  • MCP Clients (like your Haystack Agent) handle everything from connection management to calling specific tools, to parsing and acting on responses.

πŸͺ» Your Spring Challenge

You’re going to create a tool-calling Haystack Agent that acts as an MCP Client β€” capable of connecting to the GitHub MCP Server and performing actions like:

  • Creating forks
  • Opening PRs
  • Reading file content
  • Updating issues
  • or whatever GitHub action you choose to empower it with ✨

πŸ”§ You’ll Use:

  • Agent – component for the smart decision-maker
  • MCPTool – lets your agent talk to the MCP Server

🎯 Requirements


You can find more info in deepset-ai/spring-into-haystack.