What is Model Context Protocol (MCP)?

MCP (Model Context Protocol) is a lightweight system that connects AI agents to real-time data sources like GitHub, LinkedIn, and more. It gives AI the live context it needs to understand, decide, and act accurately, whether it’s drafting code, responding to messages, or analyzing project status. Instead of relying only on static prompts, agents using MCP can fetch fresh data, follow dynamic workflows, and interact with external tools, making them far more reliable, useful, and context aware.

When building an AI chatbot, it's helpful to understand the architecture behind the scenes even if you're not a developer. At its core, It is composed of three main roles working in harmony: the Orchestrator, the Agent, and the Channels. Think of it like a simple machine: you input documents and flows, the Agent processes and decides, and the Channels deliver responses to your users.

The Orchestrator is like the control tower of your chatbot, intelligent, centralized, and always watching. Its job is to manage everything:

• It sets up and updates chatbot profiles (what we call Agents).

• It decides whether your bot should answer using knowledge from uploaded documents or follow a predefined flow.

• It ensures the right security and access rules are in place for who can talk to which channels, what content the chatbot can use, and when.

Technically, it acts as the brain steering multiple agent instances. Every time you create or modify a bot, it's the Orchestrator that coordinates behind the scenes.

An Agent is essentially a trained assistant powered by two possible engines:

• Knowledge-Based Engine: If you've uploaded FAQs or documents, this engine retrieves relevant answers from that content.

• Flow-Based Engine: If you've built a flow (with steps like greeting, collecting info, responding), that logic guides the conversation.

What's clever is that you can choose one of these setups or even combine them. The Orchestrator activates whichever one you select in the settings, so your bot knows how to respond.

Once configured, the Agent becomes the execution center, taking user queries, deciding whether it's a document lookup or a structured flow, and crafting the reply accordingly.

Channels are how your users actually interact with the bot: WhatsApp chat, live web chat, or embedded iframes on your site. The Agent sends its responses here. It’s like the speaker of the system, bringing your bot’s intelligence into real conversations.

When you click “deploy,” the Orchestrator links your Agent to the chosen Channels and handles the setup (like embedding a script or generating a WhatsApp link). From that moment on, any message sent via those channels goes through your Agent’s logic and returns an intelligent response.

This simple three-tier structure is powerful for a few reasons:

• Flexibility: You can switch between knowledge-based answers and structured flows as needed.

• Scalability: You can deploy the same Agent across multiple channels simultaneously.

• Clarity: Each part of the system has a defined role, making it easier to maintain and extend.

• Security: The Orchestrator ensures your content and flows are only used in appropriate ways and channels.

The GitHub MCP Server is a system that lets AI tools like Copilot or Claude connect directly to GitHub and perform tasks like reading issues, creating pull requests, checking code security, or managing repositories.

It acts like a bridge between AI and GitHub, giving the AI controlled access to specific GitHub features. You can choose what the AI can do by enabling specific “toolsets” like Issues, Repositories, Pull Requests, etc.

With this integration, your Agent can:

• Explore repositories: Read files, understand folder structures, and even summarize code.

• Manage issues & PRs: Create, update, and close issues, or review pull requests with AI-powered insights.

• Track workflows: Get real-time updates from GitHub Actions, debug failed runs and keep release cycles on track.

• Analyze code: Identify security vulnerabilities, review patterns, and simplify documentation with AI assistance.

• Support team collaboration: Fetch discussions, monitor team activity, and notify members automatically.

The GitHub MCP Server plugs into Agent system. Here’s the flow:

1. User Query → A developer or team member types a natural language request (e.g., “Check last failed build in repo Alpha”).

2. Orchestrator Routing → Agent decides this request requires GitHub access and sends it to the GitHub MCP Server.

3. GitHub MCP Server → Talks directly to GitHub’s APIs and fetches the right data (commits, workflows, issues, etc.).

4. Agent Response → The Agent interprets the result and sends back a clear, human-friendly answer.

So instead of raw technical logs, you get a plain-language response like:

“Build #248 in the Alpha repo failed because of a missing environment variable".