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15. Model Context Protocol (MCP) Deep Dive

MCP architecture, connection types, and production patterns.

Blog Post: https://arjunprabhulal.com/adk-mcp-deep-dive/

Table of Contents

  1. What is MCP?
  2. Prerequisites
  3. Setup Steps
  4. Connection Types
  5. Running the Agent
  6. Next Steps

What is MCP?

Model Context Protocol is a standardized way for AI agents to connect to external services and data sources. Key benefits:

  • Standardized interface - One protocol for many services
  • Dynamic tool discovery - Agents learn available tools at runtime
  • Secure connections - Built-in authentication support

Prerequisites

  • Python 3.10+
  • Gemini API key from AI Studio
  • GitHub Personal Access Token
  • Node.js (for Firecrawl MCP - optional)

Setup Steps

  1. Navigate to this module:
cd 15-mcp-deep-dive
  1. Create and activate a virtual environment:
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. Install dependencies:
pip install -r ../requirements.txt
  1. Set up environment variables in mcp_agent/.env:
GOOGLE_API_KEY=your-api-key-here
GITHUB_TOKEN=your-github-token
FIRECRAWL_API_KEY=your-firecrawl-api-key  # Optional

Connection Types

Type Class Use Case
HTTP StreamableHTTPServerParams Cloud-hosted MCP servers (GitHub Copilot MCP)
Stdio StdioConnectionParams Local process-based servers (npx tools)

HTTP Connection Example

StreamableHTTPServerParams(
    url="https://api.githubcopilot.com/mcp/",
    headers={"Authorization": f"Bearer {token}"},
)

Stdio Connection Example

StdioConnectionParams(
    server_params=StdioServerParameters(
        command="npx",
        args=["-y", "firecrawl-mcp"],
    ),
)

Running the Agent

Using Programmatic Runner

cd mcp_agent
python agent.py

Test Queries:

  • "Explain MCP and its use in ADK"
  • "Find ADK repositories on GitHub"

Next Steps

Continue to 16. Session, State & Memory