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Unlock the power of MCP—connect data dots, break silos, and let your agents deliver smarter, real-time answers

Introduction

As a passionate innovator in AI systems, I’m on a mission to redefine how intelligent agents interact with the world—moving beyond isolated prompts to create context-aware, dynamic solutions. With Model Context Protocol (MCP), I aim to empower agents to seamlessly connect, interpret, and unify fragmented data sources, enabling them to think, plan, and collaborate intelligently across domains.

Intelligent agents are transforming industries by automating tasks, enhancing decision-making, and delivering personalized experiences. However, their effectiveness is often limited by their ability to access and interpret data from multiple, fragmented sources. This is especially true in many enterprise organizations, where data is distributed across various systems and formats. Enter Model Context Protocol (MCP) — a breakthrough that enables agents to interact with diverse data sources in a unified, context-aware manner. 

In this article, you will see how healthcare industry leverages MCP to interact with distributed data.

Why Agents Struggle to Connect to Diverse Data Sources 

Agents typically rely on structured inputs to generate meaningful responses. But in real-world scenarios, data is: 

• Scattered across systems: EHRs, lab systems, imaging platforms, insurance databases. 
• Stored in different formats: HL7, FHIR, JSON, XML, CSV. 
• Accessed via varied protocols: REST APIs, GraphQL, SOAP, file-based systems. 

Key Challenges: 

1. Context Loss – Agents often lack the ability to maintain context across multiple data sources. 
2. Semantic Misalignment – Different systems may use different terminologies for the same concept. 
3. Latency & Real-Time Needs – Some data must be accessed instantly (e.g., patient vitals). 
4. Security & Access Control – Varying authentication mechanisms and data governance policies. 
5. Scalability – Custom integrations are hard to maintain and scale across organizations. 

How MCP Helps Agents Overcome Data Source Challenges – Simply Explained 

Imagine an agent as a smart assistant trying to answer questions or perform tasks. To do this well, it needs to understand where the data is, what it means, and how it connects to the user’s query. That’s where Model Context Protocol (MCP) steps in. 

MCP is like a smart translator and coordinator between the agent and multiple data sources. It helps the agent: 

• Understand the context of the user’s request 

• Know which data sources to talk to 

• Interpret different formats and meanings 

• Keep everything connected and relevant 

How MCP Solves the Challenges 

Let’s revisit the challenges and see how MCP helps:

Data Silos 

Problem: Data is scattered across systems that don’t talk to each other. 

MCP’s Help: MCP creates a unified context so the agent can pull data from different places and treat it as one coherent story.

Inconsistent Formats 

Problem: One system uses HL7, another uses JSON and another uses FHIR. 

MCP’s Help: MCP normalizes these formats so the agent doesn’t get confused. It translates them into a format, the model understands.

Semantic Misalignment 

Problem: “Blood sugar” in one system is “glucose level” in another. 

MCP’s Help: MCP maps different terms to the same meaning, so the agent knows they’re talking about the same thing.

Security & Compliance 

Problem: Each system has its own access rules and privacy requirements. 

MCP’s Help: MCP respects access controls and ensures the agent only sees what it’s allowed to, keeping everything secure and compliant.

Context Loss 

Problem: When switching between systems, agents lose track of what the user asked. 

MCP’s Help: MCP maintains a continuous context, so the agent remembers the user’s intent and keeps the conversation relevant. 

Healthcare Example: Post-Surgery Monitoring Agent 

Let’s say a doctor asks the agent: “Show me post-op patients with abnormal vitals and pending lab results.”

Without MCP: 

• The agent might pull vitals from one system, lab results from another, and struggle to connect them. 

• It might not understand that “pending labs” in one system means “awaiting results” in another. 

With MCP: 

• The agent knows where to look, how to interpret, and how to connect the data. 

• It gives a clear, accurate answer — saving time and improving patient care. 

Here’s a simplified Python example for your healthcare scenario: 

from typing import Dict, Any
import requests

class MCPAgent:
    def __init__(self):
        self.context = {}

    def update_context(self, query: str):
        self.context['query'] = query

    def fetch_ehr_data(self):
        # Simulate EHR API call
        return {"patients": [{"id": 1, "status": "post-op"}]}

    def fetch_vitals(self):
        # Simulate wearable device API call
        return {"patients": [{"id": 1, "vitals": "abnormal"}]}

    def fetch_lab_results(self):
        # Simulate lab system API call
        return {"patients": [{"id": 1, "lab": "pending"}]}

    def process_query(self):
        ehr = self.fetch_ehr_data()
        vitals = self.fetch_vitals()
        labs = self.fetch_lab_results()

        # Merge data based on patient ID
        combined = []
        for patient in ehr['patients']:
            pid = patient['id']
            vitals_info = next((v for v in vitals['patients'] if v['id'] == pid), {})
            lab_info = next((l for l in labs['patients'] if l['id'] == pid), {})
            combined.append({**patient, **vitals_info, **lab_info})

        return combined

# Usage
agent = MCPAgent()
agent.update_context("Show me post-op patients with abnormal vitals and pending lab results")
result = agent.process_query()
print(result)

If you’re not a developer, the main idea is that MCP helps agents connect information from different systems, so you get answers that make sense, no matter where the data comes from.

Core Component in the code

Context Manager 

Purpose: Keeps track of the user’s query and any relevant information across multiple data sources. 

Why: Without context, the agent would treat each data source independently and fail to connect the dots.

def update_context(self, query: str):
    self.context['query'] = query

This stores the user’s intent, so the agent knows what to look for. 

Data Source Connectors 

Purpose: Interfaces to fetch data from different systems (EHR, wearable devices, lab systems). 

Why: Each system has its own API or format. Connectors standardize how the agent retrieves data. 

def fetch_ehr_data(self):
    # Simulate EHR API call
    return {"patients": [{"id": 1, "status": "post-op"}]}

def fetch_vitals(self):
    # Simulate wearable device API call
    return {"patients": [{"id": 1, "vitals": "abnormal"}]}

def fetch_lab_results(self):
    # Simulate lab system API call
    return {"patients": [{"id": 1, "lab": "pending"}]}

In real-world use, this would call an API endpoint and return structured data. 

Contextual Data Binding & Normalization 

Purpose: Merge and normalize data from multiple sources into a single, meaningful response. 

Why: Different systems use different terms and formats. MCP ensures semantic alignment. 

def process_query(self):
    ehr = self.fetch_ehr_data()
    vitals = self.fetch_vitals()
    labs = self.fetch_lab_results()

    # Merge data based on patient ID
    combined = []
    for patient in ehr['patients']:
        pid = patient['id']
        vitals_info = next((v for v in vitals['patients'] if v['id'] == pid), {})
        lab_info = next((l for l in labs['patients'] if l['id'] == pid), {})
        combined.append({**patient, **vitals_info, **lab_info})

    return combined

This merges patient data from all sources into one unified view. 

LLM Integration with MCP Host

# mcp_host.py
import openai
from mcp_client import MCPClient
import os

openai.api_type = "azure"
openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT")
openai.api_key = os.getenv("AZURE_OPENAI_KEY")
openai.api_version = "2023-05-15"

class MCPHost:
    def __init__(self, client):
        self.client = client
        self.context = {}

    def interpret_query(self, query):
        response = openai.ChatCompletion.create(
            engine=os.getenv("AZURE_OPENAI_DEPLOYMENT"),
            messages=[
                {"role": "system", "content": "You are an MCP orchestrator. Map user queries to EHR, vitals, and lab tools."},
                {"role": "user", "content": query}
            ]
        )
        return response['choices'][0]['message']['content']

    def process_query(self, query):
        self.context['query'] = query
        interpretation = self.interpret_query(query)
        print(f"LLM Interpretation: {interpretation}")

        # Call MCP tools
        ehr = self.client.fetch_ehr()
        vitals = self.client.fetch_vitals()
        labs = self.client.fetch_labs()

        combined = []
        for patient in ehr['patients']:
            pid = patient['id']
            vitals_info = next((v for v in vitals['patients'] if v['id'] == pid), {})
            lab_info = next((l for l in labs['patients'] if l['id'] == pid), {})
            combined.append({**patient, **vitals_info, **lab_info})

        return combined

# Usage
client = MCPClient("http://127.0.0.1:8000")
host = MCPHost(client)
result = host.process_query("Show me post-op patients with abnormal vitals and pending lab results")
print(result)

If you’re not a developer, the main idea is that MCP helps agents connect information from different systems, so you get answers that make sense, no matter where the data comes from

User Query → Azure OpenAI

• The LLM interprets the query and decides which MCP tools to call.
• Example interpretation: “Fetch EHR, vitals, and lab data for post-op patients.”

Host → Client → Server

• Host uses MCP Client to fetch data from MCP Server.

Host Combines Data

• Merges EHR, vitals, and lab results into a unified response.

Beyond Healthcare: MCP in Action Across Industries

MCP’s versatility extends far beyond healthcare. For example:

Finance: MCP enables agents to unify customer data across multiple banking systems, providing a 360-degree view for personalized financial advice and streamlined compliance checks.

Manufacturing: MCP connects data from supply chain, inventory, and production systems, allowing agents to detect bottlenecks and optimize resource allocation in real time.

Retail: MCP brings together sales, inventory, and customer feedback data, empowering agents to deliver tailored promotions and improve demand forecasting.

Telecommunications: MCP integrates customer service, billing, and network performance data, enabling agents to proactively resolve issues and enhance customer satisfaction.

Energy: MCP unifies sensor, maintenance, and usage data, helping agents predict equipment failures and optimize energy distribution.

Conclusion

The Model Context Protocol (MCP) represents a pivotal step forward in enabling intelligent agents to operate seamlessly across fragmented data landscapes. By unifying access, normalizing formats, and maintaining context, MCP empowers agents to deliver accurate, timely, and context-aware insights—especially in complex domains like healthcare. As organizations continue to adopt AI-driven solutions, embracing protocols like MCP will be essential for unlocking the full potential of autonomous agents.

Citations:

What is the Model Context Protocol (MCP)? - Model Context Protocol

Architecture overview - Model Context Protocol

Azure MCP Server documentation - Azure MCP Server | Microsoft Learn

Next Steps:

• Explore MCP’s open-source documentation and sample implementations to get started.
• Experiment with integrating MCP into your own agent workflows—begin with simple connectors and expand as your needs grow.
• Stay updated on the latest advancements in agent orchestration and context management by following leading AI research forums and communities.

Further Learning:

• https://learn.microsoft.com/en-us/azure/ai-services/
• https://learn.microsoft.com/en-us/azure/developer/azure-mcp-server/
• Build Agents using Model Context Protocol on Azure | Microsoft Learn

By taking these steps, you’ll be well-positioned to build the next generation of context-aware, intelligent agents that truly connect the dots across your organization’s data.

About the author: 

I'm Juliet Rajan, a Lead Technical Trainer and passionate innovator in AI education. I specialize in crafting gamified, visionary learning experiences and building intelligent agents that go beyond traditional prompt-based systems. My recent work explores agentic AI, autonomous agents, and dynamic human-AI collaboration using platforms like Azure AI Foundry, MCP and Agents orchestration

#MSLearn #SkilledByMTT #MTTBloggingGroup

Copilot Vision brings visual, onscreen guidance to Microsoft 365 so you can see what to do, not just read about it. This inclusive approach offers clear, visual cues and step-by-step help, making it easy for everyone—regardless of learning style or ability—to confidently use Microsoft 365 Apps, Edge, Bing and more. It accelerates learning, reduces errors, and helps automate everyday work. 

Below you’ll find quick-start steps, role-based scenarios, step-by-step workflows, common pitfalls, and a metrics framework so you can quantify the impact. 

What is Copilot Vision? 

Copilot Vision is the next evolution of Microsoft 365 Copilot. Instead of switching to a help article, you get onscreen highlights and cues that show you exactly where to click and what to do. Paired with “Hey Copilot” voice activation and timesaving local file actions, Vision turns common tasks into intuitive, guided experiences. 

Why it’s different: 

• Visual overlays meet you where you work (Excel, Word, PowerPoint, Teams, OneDrive, Outlook and more). 

• Context stays on screen, so you don’t lose focus jumping between windows. 

• Handsfree options reduce friction and keep you in flow. 

 Why Copilot Vision matters  

Think of Vision as a productivity multiplier rather than a convenience feature: 

1. Eliminates guesswork – You see the exact control to use; the instant you need it. 
2. Accelerates learning – Every guided step is a microlesson; you retain workflows faster. 
3. Reduces cognitive load – No context switching to search for help to cut fatigue and errors. 
4. Enables self-service – Fewer “how do I…?” questions and less waiting on others. 
5. Drives consistency – Standardized steps reduce rework and support compliance goals. 

 Everyday benefits you can feel 

• Save time: Vision turns multi-click hunts into guided taps. 

• Reduce frustration: You don’t need to memorize where the control lives. 

• Stay in flow: Guidance lives on top of your work, not in another window. 

• Work smarter: Pair Vision with local file actions to remove mind-numbing busy work. 

Top Copilot Vision Use Cases by M365 Application 

Role based scenarios (real work, real wins) 

Finance Analyst 

You receive an export with 20 columns and inconsistent headers. 

• Use Vision to clean data, format as a table, and build a PivotTable grouped by month. 

• Then ask: Add a slicer for region and create a line chart for monthly revenue. 

Marketing Manager 

You need a quick campaign recap deck. 

• In PowerPoint, ask Vision to apply your brand theme, standardize slide layouts, and add a 30sec morph transition demo. 

• In Word, ask Vision to apply branded styles to your campaign summary and generate a table of contents. 

Project Manager 

You’re turning meeting notes into action. 

• In Teams, use Vision to assign tasks from chat, add due dates, and pin the task list to the channel. 

HR Generalist 

You’re polishing a policy document. 

• In Word, ask Vision to apply heading levels, create a TOC, and insert a two-column comparison table for policy changes vs. previous version. 

Sales Representative 

You need a tight one pager and a follow-up deck. 

• In Word, use Vision to format the one pager with styles and sections. 

• In PowerPoint, apply the brand theme and add a summary slide with consistent bullet styling. 

Step-by-step: First timer workflows 

A) Excel — Build a PivotTable (first time)

1. Open Excel and your dataset. 
2. Start Copilot and (if available) enable Vision in settings. 
3. Ask: Show me how to create a PivotTable from this dataset. 
4. Follow the highlights: Vision points to Insert → PivotTable, suggests a range, and helps choose the location. 
5. Refine: Add total revenue to values and region to rows. Now add a chart. 

B) Word — Format a report quickly

1. Open Word with your draft. 
2. Start Copilot and enable Vision (if available). 
3. Ask: Apply a professional style set and show me how to format headings consistently. 
4. Follow the highlights: Vision points to Design → Style Set and Home → Styles for Heading 1/2/3. 
5. Extend: Create a table of contents and guide me to insert a 2column table for milestones and owners. 

Advanced tips & “Power Moves” 

• Voice + Vision: Say “Hey Copilot, show me how to…” to keep hands free while you follow highlights. 

• Vision + Actions: After learning a workflow visually, offload repeats: 
  “Create a folder called ‘Approved Q4 Assets’, move today’s files into it, and rename them with yyyymmdd suffix.” 

• Vision + Power Automate: Once a workflow is well understood, automate it end-to-end (i.e. save a Power BI export → format in Excel → drop result in a Teams channel). 

• Iterate your prompts: Be explicit: include the artifact (“this dataset,” “this draft”) and the goal (“apply style set,” “add slicer”). 

• Stay privacy aware: If your file contains sensitive info, confirm your organization’s label and policy posture before sharing outputs. 

 Common pitfalls (and how to avoid them) 

• Vague Prompts → Vague guidance 
  Fix: State the object + action + outcome (e.g., “this dataset,” “create PivotTable,” “group by month”). 

• Assuming Vision is Recall 
  Fix: They’re different concepts. Vision provides onscreen guidance; ask your admin how Vision is configured in your tenant. 

• Out-of-date apps 
  Fix: Update Microsoft 365 apps and Copilot on Windows regularly to access the latest Vision capabilities. 

• Skipping governance context 
  Fix: If you work with sensitive content, understand how MIP labels and Purview apply in your org. 

Privacy & governance note 

Copilot experiences respect your organization’s data governance (e.g., Microsoft Information Protection labels and Microsoft Purview policies). Ask your admin how your tenant is configured if you’re unsure. 

Where Copilot Vision is headed 

Visual guidance is a step toward agentic workflows—systems that can see context, reason about goals, and act across your stack. Expect expanding app coverage, richer controls, smarter suggestions, and deeper ties to automation and governance so teams can move from “show me how” to “do this for me—safely.” 

Keep exploring: 

• Learn the fundamentals → https://learn.microsoft.com/microsoft-365/copilot/ 

• Business user learning path → https://learn.microsoft.com/training/paths/copilot-business-user/ 

• Get hands-on as a business user → https://learn.microsoft.com/training/paths/copilot-business-user/ 

FAQs 

Q: Do I need special licensing? 
A: You need access to Microsoft 365 Copilot. Check with your admin or licensing portal. License Options for Microsoft 365 Copilot 

Q: Is my data safe? 
A: Copilot experiences respect your organization’s data protections (e.g., MIP/Purview). If unsure, confirm your tenant settings. Data, Privacy, and Security for Microsoft 365 Copilot 

Q: I don’t see Vision yet. 
A: Ensure your apps are updated and Vision is available in your region/tenant. If it still doesn’t appear, ask your admin about enablement status. Using Copilot Vision with Microsoft Copilot 

Where are you on your AI journey? Share your story in the comments or connect with me on LinkedIn Barbara Andrews LinkedIn with #AzureAIJourney, #MicrosoftLearn #SkilledByMTT #MTTBloggingGroup 

Electron 39.0.0 has been released! It includes upgrades to Chromium 142.0.7444.52, V8 14.2, and Node 22.20.0.

The Electron team is excited to announce the release of Electron 39.0.0! You can install it with npm via npm install electron@latest or download it from our releases website. Continue reading for details about this release.

If you have any feedback, please share it with us on Bluesky or Mastodon, or join our community Discord! Bugs and feature requests can be reported in Electron's issue tracker.

Notable Changes​

Stack Changes​

• Chromium 142.0.7444.52
  • New in 142
  • New in 141
• Node 22.20.0
  • v22.20.0 Announcement
  • v22.19.0 Announcement
• V8 14.2
  • V8 roll increment

Electron 39 upgrades Chromium from 140.0.7339.41 to 142.0.7444.52, Node.js from 22.18.0 to v22.20.0, and V8 from 14.0 to 14.2.

ASAR Integrity graduates to stable​

A long-standing "experimental" feature -- ASAR integrity -- is now stable in Electron 39. When you enable this feature, it validates your packaged app.asar at runtime against a build-time hash to detect any tampering. If no hash is present or if there is a mismatch in the hashes, the app will forcefully terminate.

See the ASAR integrity documentation for full information on how on the feature works, on how to use it in your application, and how to use it in Electron Forge and Electron Packager.

In related news, Electron Packager v19 now enables ASAR by default. #1841

New Features and Improvements​

• Added app.isHardwareAccelerationEnabled(). #48680
• Added RGBAF16 output format with scRGB HDR color space support to Offscreen Rendering. #48504
• Added methods to enable more granular accessibility support management. #48625
• Added support for USBDevice.configurations. #47459
• Added the ability to retrieve the system accent color on Linux using systemPreferences.getAccentColor. #48628
• Allowed for persisting File System API grant status within a given session. #48326 ^{(Also in 37, 38)}
• Support dynamic ESM imports in non-context isolated preloads. #48488 ^{(Also in 37, 38)}
• Marked the ASAR integrity feature as stable. It had previously been experimental. #48434

Breaking Changes​

Deprecated: --host-rules command line switch​

Chromium is deprecating the --host-rules switch.

You should use --host-resolver-rules instead.

Behavior Changed: window.open popups are always resizable​

Per current WHATWG spec, the window.open API will now always create a resizable popup window.

To restore previous behavior:

webContents.setWindowOpenHandler((details) => {
  return {
    action: 'allow',
    overrideBrowserWindowOptions: {
      resizable: details.features.includes('resizable=yes'),
    },
  };
});

Behavior Changed: shared texture OSR paint event data structure​

When using the shared texture offscreen rendering feature, the paint event now emits a more structured object. It moves the sharedTextureHandle, planes, modifier into a unified handle property. See the OffscreenSharedTexture documentation for more details.

End of Support for 36.x.y​

Electron 36.x.y has reached end-of-support as per the project's support policy. Developers and applications are encouraged to upgrade to a newer version of Electron.

What's Next​

In the short term, you can expect the team to continue to focus on keeping up with the development of the major components that make up Electron, including Chromium, Node, and V8.

You can find Electron's public timeline here.

More information about future changes can be found on the Planned Breaking Changes page.

GKE turned 10 in 2025! In this episode, we talk with GKE PM Gari Singh about GKE's journey from early container orchestration to AI-driven ops. Discover Autopilot, IPPR, and a bold vision for the future of Kubernetes.

Do you have something cool to share? Some questions? Let us know:

• web: kubernetespodcast.com

• mail: kubernetespodcast@google.com

• X: @kubernetespod

• bluesky: @kubernetespodcast.com

News of the week

• Cloud Native Computing Foundation Announces Knative's Graduation

• llm-d 0.3: Wider Well-Lit Paths for Scalable Inference

• vllm-project/semantic-router on github

• Announcing the Certified Meshery Contributor (CMC)

• Introducing Headlamp Plugin for Karpenter - Scaling and Visibility

Links from the interview

• Kelsey Hightower's Kubernetes the Hard Way

• MiniKube

• Kind

• Docker Compose

• Docker Swarm

• GKE Autopilot

• Dynamic Resource Allocation (DRA)

• Google Cloud TPUs

• Node Auto Provisioning (GKE)

• Jax (Machine Learning Framework)

• Horizontal Pod Autoscaling (HPA)

• Serverless on Google Cloud

• Grafana

• Prometheus

• Kubectl-ai

• Vertical Pod Autoscaler (VPA)

• Kubernetes v1.33: In-Place Pod Resize Graduated to Beta

• In-place Vertical Scaling of Pods - Resize CPU and Memory Resources assigned to Containers

• GKE under the hood: Container-optimized compute delivers fast autoscaling for Autopilot

Open-source

Make Your GitHub Profile Update Itself (WordPress posts, GitHub releases, LinkedIn newsletters) by Chris Woody Woodruff

Software Architecture

8 platform engineering anti-patterns by Bill Doerrfeld

.NET

All About Code Cleanup (YouTube) by Microsoft Visual Studio

A Small Update by Shawn Wildermuth

Announcing Sponsorship on NuGet.org by .NET Team

Modernizing Visual Studio Extension Compatibility: Effortless Migration for Extension Developers and Users by Tina Schrepfer

Adding metadata to fallback endpoints in ASP.NET Core by Andrew Lock

Thread-Safe Initialization with LazyInitializer by Gérald Barré

Cache me if you can: a look at deployment state in Aspire by Safia Abdalla

Using SignalR with Wolverine 5.0 ΓÇô The Shade Tree Developer by Jeremy D. Miller

REST/APIs

The Interface Is No Longer the Code by Mike Amundsen

Software Development

Windows Runtime design principle: Properties can be set in any order by Raymond Chen

Windows

Don't let your PC suffer, run these Windows commands regularly by Pankil Shah

Databases

Compare PostgreSQL Databases in Seconds (YouTube) by Database Star

Cloud

AWS Outage Was 'Inevitable,' Says Former AWS, Google Exec by Mark Haranas