Practical Guide to Model Context Protocol (MCP)

What exactly is MCP?

Model Context Protocol (MCP) is an open-source framework designed to let Large Language Models (LLMs) - like Claude or GPT- securely and efficiently interact with external data sources such as APIs, databases, and codebases. Rather than relying on manual processes or ad-hoc integrations, MCP provides a structured way to connect LLMs with deterministic external tools.


Under the hood

MCP Server

  • Serves as a secure, lightweight intermediary for managing API calls, authentication, and data retrieval.
  • Deployable via common environments including Docker containers, Node.js servers, Python Flask, or FastAPI applications.
  • Exposes clearly defined /tools endpoints describing the available functionality (e.g., fetching commits, database queries).

MCP Client

  • Acts as the mediator between the LLM and MCP server.
  • Dynamically discovers available tools from the server and executes tool-specific requests.
  • Retrieves structured external data, forwarding it to the LLM for further synthesis.

Large Language Model (LLM)

  • Pure reasoning engine with no direct external connectivity or credential management.
  • Utilizes MCP’s structured interactions to request and interpret external data.
  • Generates meaningful and context-rich responses based on retrieved information.

Security Principles

  • Credential Isolation: Sensitive tokens and API keys remain securely on the MCP server, invisible to clients and LLMs.
  • Controlled Tool Exposure: Clearly defined JSON schemas prevent arbitrary actions, ensuring tight security controls.

Extensibility & Customization

  • Prebuilt Servers: Available for popular platforms like GitHub (commit summaries, pull requests), Postgres (structured queries), and Slack (message interactions).
  • Rapid Custom Development: Custom MCP servers for internal integrations typically require fewer than 100 lines of code, accelerating enterprise-specific solutions.
  • Standardization Advantage: The consistent MCP structure simplifies maintenance and reduces the technical debt often encountered with ad-hoc integrations.


MCP Workflow: A GitHub Example

Here's how MCP seamlessly facilitates real-time data integration, illustrated with a practical GitHub use case:

1. User Query

"Summarize the latest 3 commits in microsoft/vscode."

2. Client-Server Communication

  • The MCP client queries the server’s /tools endpoint.
  • Server response example:
{
  "tools": [
    {
      "name": "list_commits",
      "description": "Fetches commits via GitHub’s REST API",
      "parameters": {
        "owner": "string",
        "repo": "string",
        "branch": "string | null"
      }
    }
  ]
}

3. LLM Tool Request

Claude generates a deterministic, structured request:

{
  "tool": "list_commits",
  "params": {
    "owner": "microsoft",
    "repo": "vscode",
    "branch": "main"
  }
}

4. Data Retrieval

  • MCP server securely authenticates (credentials remain hidden).
  • Executes GitHub API request: GET /repos/microsoft/vscode/commits.
  • Returns commit data as structured JSON to the MCP client.

5. Synthesis & Response

  • Client passes retrieved data back to Claude.
  • Claude synthesizes and provides a clear response:
1. [feat]: Add support for new extension debugging features (SHA: e1f2g3h)
2. [fix]: Resolve markdown rendering issue in editor (SHA: i4j5k6l)
3. [docs]: Update README with installation steps (SHA: m7n8o9p)

The Real-Time Data Challenge for LLMs

LLMs inherently lack direct access to real-time external data. Without MCP, engineers typically resort to:

  • Manual workflows: Manually fetching, injecting, and parsing data.
  • Brittle solutions: Custom scripts, prone to breaking and challenging to maintain.
  • Lack of consistency: Each new integration (GitHub, Slack, DBs) demands bespoke, non-standard setups.

MCP directly addresses these pain points by providing a unified, secure, and reliable integration mechanism.

Moving Forward with MCP

Incorporating MCP into your engineering processes simplifies complex integrations and expands the potential of your systems by leveraging the reasoning abilities of LLMs. Whether your goal is custom internal tooling, sophisticated multi-step workflows or tighter IDE integration, MCP provides a stable and adaptable foundation.