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💡 Event Description

Join Ilgar Zarbaliyev, Microsoft MVP & MCT Regional Lead, for an exclusive Microsoft Season of AI session dedicated to Model Context Protocol (MCP) — the foundation behind modern AI integrations. This one-hour webinar is built on practical insights from the “Complete MCP Guide” course and walks you through everything from understanding MCP architecture to building your own MCP Servers and Clients in Python.

🧭 What You’ll Learn

In this session, you’ll explore:

• What MCP Really Is: Architecture, history, and why it’s becoming critical in AI interoperability.
• From Theory to Implementation: How MCPs connect models, clients, and servers seamlessly.
• Python SDK in Action: Building MCP solutions using Python (applicable across SDKs).
• Step-by-Step Development: Create real MCP Servers and Clients with practical examples.
• Streamable HTTP Transport: Understand the newest protocol updates and how they improve efficiency.
• Hands-on Insights: Lessons from building 4+ complete MCP servers and clients.

🧠 Why This Session Stands Out

• Complete Journey: From zero knowledge to expert-level understanding in one structured flow.
• Focused on Python: Learn the cleanest, most flexible SDK for MCP development.
• Updated Content: Covers all the latest MCP advancements.
• Community-Driven: Designed for Microsoft AI & Data enthusiasts exploring MCP as part of the new AI wave.

​LLMs are powerful, but they still hallucinate facts, especially when asked about entities, relationships, or claims that require up-to-date or structured knowledge.

​In this hands-on workshop, we'll explore how to use Wikidata as a grounding and fact-checking layer for LLMs to reduce hallucinations and make AI systems more reliable.

​We'll start with a short introduction to Wikidata and then set up the Wikidata MCP so an LLM can retrieve and verify facts rather than relying solely on its internal memory. This already provides a practical way to ground LLM outputs in verifiable data.

​From there, we’ll go beyond LLM-only approaches and build a small experimental fact-checking pipeline. The system combines semantic retrieval, LLM-based reranking, and natural language inference (NLI) to validate claims against evidence in a more controlled and interpretable way.

​This workshop focuses on evidence-driven verification pipelines that make LLM's reasoning steps explicit and easier to inspect, debug, and improve.

​What we'll cover:

• ​Wikidata as a structured source for factual verification
• ​Setting up and querying Wikidata using MCP
• ​Verifying claims with MCP + an LLM
• ​Moving beyond pure GenAI to evidence-based fact-checking
• ​Finding relevant Wikidata statements with semantic search
• ​Ranking candidate evidence with an LLM
• ​Verifying claims using an NLI model

​​What you'll leave with By the end of the workshop, you'll be able to:

• Ground LLM outputs in structured data to reduce hallucinations
• ​Understand when LLM-only fact-checking is not enough
• ​Build a small, transparent fact-checking pipeline you can adapt to real projects

​About the speaker:

​Philippe Saadé is the AI/ML project manager at Wikimedia Deutschland. His current work focuses on making Wikidata accessible to AI application with projects like the Wikidata vector database and the Wikidata Model Context Protocol.

Join our Slack: https://datatalks.club/slack.html ​

​This event is sponsored by Wikimedia

Register: https://www.eventbrite.co.uk/e/agents-tools-and-mcp-oh-my-next-level-ai-concepts-for-developers-tickets-1977874016426

AI is evolving fast, and so are the ways developers can integrate it into tech systems.

1st Talk - Agents, Tools, and MCP, oh my! Next-level AI concepts for developers Jennifer Reif - Developer Advocate at Neo4j AI is evolving fast, and so are the ways developers can integrate it into tech systems. A flurry of new approaches and tools surfaces every week, and it’s hard to know where to focus. In this session, we’ll pull back the curtain on the next wave of AI development with agents, tool integrations, and Model Context Protocol (MCP). We’ll break down what AI agents are, how they interact with tools and APIs, and why context is critical for building smarter, more reliable applications. Next, we’ll look at MCP and how it standardizes communication between AI models and external systems. Along the way, we’ll touch on related concepts and step through code and demos, giving you a complete roadmap to level up your AI skills. You won’t need a yellow brick road to follow along, but you will discover some magical new tricks to level up your AI skills! Jennifer Reif is a Developer Advocate at Neo4j, speaker, and blogger with an MS in CMIS. An avid developer and problem-solver, she has worked with many businesses and projects to organize and make sense of widespread data assets and leverage them for maximum business value. She has expertise in a variety of commercial and open source tools, and she enjoys learning new technologies, sometimes on a daily basis! Her passion is finding ways to organize chaos and deliver software more effectively.

2nd Talk - Graph Data Science: How & Why Arthur Bigeard and CEO of G.V() - Founder Graph analytics is a major area of data science. In this talk, we’ll discuss the use cases that best apply to graph analytics, how to get started and what tools are available to support you in learning and applying graph data science. We’ll cover some of the most popular graph data science libraries and algorithms available, and how to run them on a sample dataset. We’ll briefly introduce graph databases in the mix as well as how organizations leverage them to deliver cutting edge solutions for their industry. Arthur Bigeard is the Founder and CEO of G.V(), a Glasgow-based startup building the next generation of tooling for knowledge graphs. His mission is to create universal, plug-and-play graph database solutions to simplify and accelerate the day-to-day of working with graph data.

The next evolution of agentic AI isn’t just “better prompts” or “more tools,” it’s agents that can collaborate across boundaries. The A2A (Agent-to-Agent) Protocol makes that collaboration practical by standardizing how agents discover each other, negotiate capabilities, exchange tasks, stream progress, and return artifacts — even when they’re built on different frameworks or run in different environments.

In this session, we’ll unpack why many multi-agent systems fail in production (fragile handoffs, unclear responsibilities, brittle integrations, and poor reliability under long-running workflows). Then we’ll introduce the core A2A building blocks — Agent Cards, task lifecycles, streaming updates, artifact delivery, and secure interoperability and show how to orchestrate multiple specialist agents with clear contracts and robust coordination patterns.

A live walkthrough will demonstrate how to design a Supervisor + Specialist architecture using A2A, including real-time progress streaming, error recovery, and observable “handoffs” that make multi-agent workflows durable instead of demo-only.

What We Will Cover:

• Why multi-agent systems fail in production – context loss, inconsistent handoffs, poor visibility, and unreliable sub-task delegation
• A2A Protocol Fundamentals – standardizing agent discovery, capability signaling, tasks, artifacts, and streaming
• Agent Discovery with Agent Cards – skills, modalities, endpoints, versioning, and trust boundaries
• Scalable Orchestration Patterns – Supervisor → Router → Specialist teams, and contract-based delegation with clear inputs/outputs
• Tool-Using Agents vs Agent-to-Agent Collaboration – understanding how A2A complements MCP
• Long-Running Task Management – designing task lifecycles, handling partial outputs, cancellations, retries, fallbacks, and resumable execution
• Streaming Progress & Artifact Delivery – real-time updates and structured outputs you can store and reuse
• Production Considerations – observability, debugging, governance, authentication boundaries, and safety guardrails for agent networks

Hands-On Insights:

Through a guided demo and Q&A, you’ll learn how to:

• Stand up a simple A2A orchestrator agent that discovers specialist agents via their Agent Cards
• Delegate work across multiple agents with reliable handoffs
• Stream progress updates and collect artifacts (reports, structured data, intermediate reasoning outputs)
• Implement practical resilience (timeouts, retries, fallback agents, and error-aware routing)

You’ll leave with a clear mental model and a reusable orchestration blueprint to evolve from single-agent demos into durable multi-agent systems.

The next evolution of agentic AI isn’t just “better prompts” or “more tools,” it’s agents that can collaborate across boundaries. The A2A (Agent-to-Agent) Protocol makes that collaboration practical by standardizing how agents discover each other, negotiate capabilities, exchange tasks, stream progress, and return artifacts — even when they’re built on different frameworks or run in different environments.

In this session, we’ll unpack why many multi-agent systems fail in production (fragile handoffs, unclear responsibilities, brittle integrations, and poor reliability under long-running workflows). Then we’ll introduce the core A2A building blocks — Agent Cards, task lifecycles, streaming updates, artifact delivery, and secure interoperability and show how to orchestrate multiple specialist agents with clear contracts and robust coordination patterns.

A live walkthrough will demonstrate how to design a Supervisor + Specialist architecture using A2A, including real-time progress streaming, error recovery, and observable “handoffs” that make multi-agent workflows durable instead of demo-only.

What We Will Cover:

• Why multi-agent systems fail in production – context loss, inconsistent handoffs, poor visibility, and unreliable sub-task delegation
• A2A Protocol Fundamentals – standardizing agent discovery, capability signaling, tasks, artifacts, and streaming
• Agent Discovery with Agent Cards – skills, modalities, endpoints, versioning, and trust boundaries
• Scalable Orchestration Patterns – Supervisor → Router → Specialist teams, and contract-based delegation with clear inputs/outputs
• Tool-Using Agents vs Agent-to-Agent Collaboration – understanding how A2A complements MCP
• Long-Running Task Management – designing task lifecycles, handling partial outputs, cancellations, retries, fallbacks, and resumable execution
• Streaming Progress & Artifact Delivery – real-time updates and structured outputs you can store and reuse
• Production Considerations – observability, debugging, governance, authentication boundaries, and safety guardrails for agent networks

Hands-On Insights:

Through a guided demo and Q&A, you’ll learn how to:

• Stand up a simple A2A orchestrator agent that discovers specialist agents via their Agent Cards
• Delegate work across multiple agents with reliable handoffs
• Stream progress updates and collect artifacts (reports, structured data, intermediate reasoning outputs)
• Implement practical resilience (timeouts, retries, fallback agents, and error-aware routing)

You’ll leave with a clear mental model and a reusable orchestration blueprint to evolve from single-agent demos into durable multi-agent systems.

The next evolution of agentic AI isn’t just “better prompts” or “more tools,” it’s agents that can collaborate across boundaries. The A2A (Agent-to-Agent) Protocol makes that collaboration practical by standardizing how agents discover each other, negotiate capabilities, exchange tasks, stream progress, and return artifacts — even when they’re built on different frameworks or run in different environments.

In this session, we’ll unpack why many multi-agent systems fail in production (fragile handoffs, unclear responsibilities, brittle integrations, and poor reliability under long-running workflows). Then we’ll introduce the core A2A building blocks — Agent Cards, task lifecycles, streaming updates, artifact delivery, and secure interoperability and show how to orchestrate multiple specialist agents with clear contracts and robust coordination patterns.

A live walkthrough will demonstrate how to design a Supervisor + Specialist architecture using A2A, including real-time progress streaming, error recovery, and observable “handoffs” that make multi-agent workflows durable instead of demo-only.

What We Will Cover:

• Why multi-agent systems fail in production – context loss, inconsistent handoffs, poor visibility, and unreliable sub-task delegation
• A2A Protocol Fundamentals – standardizing agent discovery, capability signaling, tasks, artifacts, and streaming
• Agent Discovery with Agent Cards – skills, modalities, endpoints, versioning, and trust boundaries
• Scalable Orchestration Patterns – Supervisor → Router → Specialist teams, and contract-based delegation with clear inputs/outputs
• Tool-Using Agents vs Agent-to-Agent Collaboration – understanding how A2A complements MCP
• Long-Running Task Management – designing task lifecycles, handling partial outputs, cancellations, retries, fallbacks, and resumable execution
• Streaming Progress & Artifact Delivery – real-time updates and structured outputs you can store and reuse
• Production Considerations – observability, debugging, governance, authentication boundaries, and safety guardrails for agent networks

Hands-On Insights:

Through a guided demo and Q&A, you’ll learn how to:

• Stand up a simple A2A orchestrator agent that discovers specialist agents via their Agent Cards
• Delegate work across multiple agents with reliable handoffs
• Stream progress updates and collect artifacts (reports, structured data, intermediate reasoning outputs)
• Implement practical resilience (timeouts, retries, fallback agents, and error-aware routing)

You’ll leave with a clear mental model and a reusable orchestration blueprint to evolve from single-agent demos into durable multi-agent systems.

Join me as I take you through my learning of MCP servers and share my knowledge and experience. The best way to learn something is to teach it, I want to know and learn more about MCP servers, so am committing to this session to share it with you.

MCP servers provide a standard way, or protocol, to connect LLMs with data, tools, and resources. The MCP website describes MCP as a universal adaptor for AI applications. In the same way that a universal adaptor lets you connect your physical devices to accessories, MCP lets you connect AI applications to other data and tools without needing to build a custom connection to each data source.

Sue Bayes, Microsoft DataPlatform MVP. Microsoft Certified: Fabric Data Engineer Associate, Fabric Analytics Engineer Associate, Azure Enterprise Data Analyst Associate, Power BI Data Analyst Associate

Over 7 years successfully working as an independent Power BI developer and data analyst within the public and private sector.

Reporting solutions range from project management, planning , financial reporting, specific service sector reporting, bespoke data cleansing and sentiment analysis.

15 years of lecturing in Business and Computing before starting my own business.

I am passionate about data in general and how we can harness information to grow business. Knowledge of R, SQL and C# but main love is Python, M and DAX.

When not in front of the screen, I love to run and walk my dog and be outside.

En esta sesión de arranque de la serie Python + MCP nos metemos de lleno con la tech más popular del 2025: MCP (Model Context Protocol).

Vamos a usar el SDK de FastMCP en Python para crear un servidor MCP local y consumirlo desde chatbots como GitHub Copilot. Luego armamos nuestro propio cliente MCP para conectarnos al servidor.

Cerramos conectando frameworks de agentes como LangGraph y Microsoft Agent Framework a servidores MCP.

In the intro session of our Python + MCP series, we dive into the hottest technology of 2025: MCP (Model Context Protocol). This open protocol makes it easy to extend AI agents and chatbots with custom functionality, making them more powerful and flexible.

We demonstrate how to use the Python FastMCP SDK to build an MCP server running locally and consume that server from chatbots like GitHub Copilot. Then we build our own MCP client to consume the server. Finally, we discover how easy it is to connect AI agent frameworks like Langchain and Microsoft agent-framework to MCP servers.

📌 This session is a part of a series. Learn more here

In the intro session of our Python + MCP series, we dive into the hottest technology of 2025: MCP (Model Context Protocol). This open protocol makes it easy to extend AI agents and chatbots with custom functionality, making them more powerful and flexible.

We demonstrate how to use the Python FastMCP SDK to build an MCP server running locally and consume that server from chatbots like GitHub Copilot. Then we build our own MCP client to consume the server. Finally, we discover how easy it is to connect AI agent frameworks like Langchain and Microsoft agent-framework to MCP servers.

📌 This session is a part of a series. Learn more here

In the intro session of our Python + MCP series, we dive into the hottest technology of 2025: MCP (Model Context Protocol). This open protocol makes it easy to extend AI agents and chatbots with custom functionality, making them more powerful and flexible.

We demonstrate how to use the Python FastMCP SDK to build an MCP server running locally and consume that server from chatbots like GitHub Copilot. Then we build our own MCP client to consume the server. Finally, we discover how easy it is to connect AI agent frameworks like Langchain and Microsoft agent-framework to MCP servers.

📌 This session is a part of a series. Learn more here

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

Important: Register on the event website to receive joining link. (rsvp on meetup will NOT receive joining link).

This is virtual event for our global community, please double check your local time. Can't make it live? Register anyway! We'll send you a recording of the webinar after the event.

Description: The AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack.

You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale.

All Sessions: Dec 4th, Dec 11th, Dec 13th, Dec 18th and Dec 20th.

Session 3 (Dec 13th) - Building AI Agents with ADK - Empowering with Tools Speaker: Arun KG (Staff Customer Engineer, GenAI, Google) Abstract: This second codelab in the "Building AI Agents with ADK" series focuses on empowering your agent with tools. You'll learn to add custom Python functions as tools, connect to real-time information using built-in tools like Google Search, and integrate tools from third-party frameworks like LangChain.

All attendees will get $5 cloud credits

From Queries to Agents: Bringing AI Workflows to SQL Server with Vectors, MCP, and Azure Optimization

AI is becoming a core part of the SQL Server ecosystem, opening new possibilities for automation, tuning, and intelligent data operations. In this talk, we’ll explore how modern AI tooling integrates with SQL Server and Azure SQL—highlighting how MCP (Model Context Protocol) servers, SQL Server’s new vector functions and out of the box Fabric Data Agents can enable low code and pro code solutions. Finally, we’ll bring these pieces together with a pro code agentic recommendation engine that looks at workload and operational pattern configuration and pricing options and proposes cost and performance optimizations for Azure SQL Database. Attendees will leave with a practical understanding of how to apply AI-driven capabilities to real SQL environments today.

Speaker: Pawel Potasinski, CTO @ InfiniteDATA Services

Model Context Protocol (MCP) is one of the hottest topics of the GenAI era. You can think of MCP as a universal translator for AI agents - just like how USB ports let you connect any device to your computer, MCP lets AI connect to any tool or service in a standardized way.

It will come as no surprise that the more powerful the tool or service, the more possibilities MCP servers provide. In this presentation you will learn about the existing MCP servers for Fabric and Power BI and how you can use these servers to support your work.

Speaker Bio: CTO at InfiniteDATA Services, a company specializing in developing state-of-the-art data platforms for large enterprises. In his professional career Pawel has always been associated with data engineering and analytics (SQL, BI, Big Data). Founder of the Polish SQL Server Users Group (PLSSUG), today known as Data Community Poland. Regular speaker at conferences, community events and user groups. Former Microsoft Most Valuable Professional (MVP).

In her tech tutorial, Elaheh explored how graph-based technologies can enhance the reasoning and transparency of agentic AI systems. She demonstrated how Graph Retrieval-Augmented Generation (Graph RAG) and the Model Context Protocol (MCP) enable richer, more structured knowledge representations. Through these methods, AI agents can make decisions that are both explainable and trustworthy. Elaheh also showcased real-world use cases—from real estate to sustainability management-highlighting how this graph-based paradigm empowers intelligent systems across industries.

This tutorial by Elaheh Momeni was held on October 14th at DSC DACH 25 in Vienna.

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