Model Context Protocol (MCP)

Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open protocol that enables secure connections between host applications (like Claude Desktop, IDEs, or other AI tools) and external data sources. It provides a standardized way for AI assistants to access and interact with various services, databases, and APIs in a controlled and secure manner.

Core Concepts

What is MCP?

• Open protocol for connecting AI assistants to external data sources
• Enables secure, controlled access to resources and tools
• Standardizes the interface between AI models and external services
• Supports both local and remote connections

Key Components:

• MCP Hosts: Applications that initiate MCP connections (e.g., Claude Desktop)
• MCP Clients: Components within hosts that maintain server connections
• MCP Servers: Services that provide resources, tools, or prompts to clients
• Resources: Data that servers expose (files, database records, etc.)
• Tools: Functions that servers can execute on behalf of clients
• Prompts: Templates that servers can provide to clients

Official Documentation and SDK

• Main Documentation: https://modelcontextprotocol.io/docs/getting-started/intro
  • Python SDK: https://github.com/modelcontextprotocol/python-sdk

MCP Python SDK

The official Python SDK for building MCP servers and clients.

Features:

• Type-safe MCP protocol implementation
• Server and client SDKs
• Built-in support for stdio and SSE transports
• Comprehensive examples and documentation
• AsyncIO support for high-performance applications

Installation:

pip install mcp

Basic Server Example:

from mcp import server, types
from mcp.server import Server
import asyncio

# Create server instance
mcp_server = Server("example-server")

@mcp_server.list_resources()
async def list_resources() -> list[types.Resource]:
    """List available resources."""
    return [
        types.Resource(
            uri="file://example.txt",
            name="Example file",
            mimeType="text/plain"
        )
    ]

@mcp_server.read_resource()
async def read_resource(uri: str) -> str:
    """Read a specific resource."""
    if uri == "file://example.txt":
        return "This is example content"
    raise ValueError(f"Unknown resource: {uri}")

# Run the server
async def main():
    async with mcp_server.stdio() as streams:
        await mcp_server.run()

if __name__ == "__main__":
    asyncio.run(main())

Use Cases:

• Building custom data connectors
• Creating tool integrations for AI assistants
• Developing secure API bridges
• Implementing domain-specific AI extensions

GitHub MCP Server

Repository: https://github.com/github/github-mcp-server

Overview

Official GitHub MCP server that provides AI assistants with controlled access to GitHub repositories and operations.

Capabilities:

• Repository browsing and file access
• Issue and pull request management
• Code search and analysis
• Branch and commit operations
• GitHub Actions integration
• User and organization information

Key Features:

• Secure Authentication: OAuth and personal access token support
• Rate Limiting: Built-in respect for GitHub API rate limits
• Permission Control: Fine-grained access control based on GitHub permissions
• Real-time Data: Access to live GitHub data and events
• Multi-Repository: Support for working across multiple repositories

Configuration:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-token-here"
      }
    }
  }
}

Available Tools:

• create_repository: Create new GitHub repositories
• create_or_update_file: Create or update files in repositories
• search_repositories: Search for repositories
• create_issue: Create new issues
• create_pull_request: Create pull requests
• fork_repository: Fork repositories
• create_branch: Create new branches

Resources Provided:

• Repository files and directories
• Issues and pull requests
• Commit history and diffs
• Repository metadata and statistics
• User and organization profiles

Example Use Cases:

• Automated code review and analysis
• Issue triage and management
• Repository documentation generation
• Code migration and refactoring assistance
• Project planning and tracking

Atlassian MCP Server

Repository: https://github.com/sooperset/mcp-atlassian

Overview

Community-developed MCP server providing integration with Atlassian products including Jira, Confluence, and Bitbucket.

Supported Products:

• Jira: Issue tracking, project management, workflows
• Confluence: Documentation, knowledge base, collaboration
• Bitbucket: Git repository management, pipelines
• Trello: Task management and boards (if supported)

Key Features:

• Multi-Product Support: Single server for multiple Atlassian services
• Authentication: Support for API tokens and OAuth
• Comprehensive API Coverage: Access to most Atlassian Cloud APIs
• Real-time Sync: Live data access and updates
• Custom Fields: Support for custom Jira fields and Confluence macros

Configuration Example:

{
  "mcpServers": {
    "atlassian": {
      "command": "node",
      "args": ["path/to/atlassian-mcp-server"],
      "env": {
        "ATLASSIAN_API_TOKEN": "your-api-token",
        "ATLASSIAN_DOMAIN": "your-domain.atlassian.net",
        "ATLASSIAN_EMAIL": "your-email@example.com"
      }
    }
  }
}

Jira Integration:

• Issue creation, updating, and querying
• Project and board management
• Sprint and epic operations
• Comment and attachment handling
• Workflow transitions
• Custom field management

Confluence Integration:

• Page creation and editing
• Space management
• Content search and retrieval
• Comment and collaboration features
• Macro and template support
• File attachment handling

Bitbucket Integration:

• Repository access and management
• Branch and pull request operations
• Pipeline management
• Code review processes
• Commit and diff analysis

Use Cases:

• Project Management: AI-assisted project planning and tracking
• Documentation: Automated documentation generation and updates
• Code Review: Intelligent code analysis and review assistance
• Issue Resolution: Smart issue categorization and resolution suggestions
• Knowledge Management: AI-powered knowledge base queries and updates
• Release Management: Automated release notes and deployment tracking

Tools and Resources:

# Example tools that might be available
tools = [
    "create_jira_issue",
    "update_jira_issue", 
    "search_jira_issues",
    "create_confluence_page",
    "update_confluence_page",
    "search_confluence_content",
    "create_bitbucket_pr",
    "merge_bitbucket_pr"
]

resources = [
    "jira://issues/{issue-key}",
    "confluence://pages/{page-id}",
    "bitbucket://repositories/{workspace}/{repo}"
]

Getting Started with MCP

1. Setting Up an MCP Server

# Install the Python SDK
pip install mcp

# Create a new MCP server project
mkdir my-mcp-server
cd my-mcp-server

# Create your server implementation
# (See Python SDK example above)

2. Configuring MCP in Claude Desktop

{
  "mcpServers": {
    "my-server": {
      "command": "python",
      "args": ["path/to/your/server.py"]
    }
  }
}

3. Testing Your Server

# Test server functionality
npx @modelcontextprotocol/inspector python path/to/your/server.py

Best Practices

Security:

• Use environment variables for sensitive credentials
• Implement proper authentication and authorization
• Validate all inputs and sanitize outputs
• Follow principle of least privilege

Performance:

• Implement proper caching strategies
• Use async/await for I/O operations
• Handle rate limiting gracefully
• Optimize resource usage

Development:

• Write comprehensive tests
• Use type hints and validation
• Implement proper error handling
• Document your APIs thoroughly

Deployment:

• Use secure transport (HTTPS/WSS)
• Monitor server health and performance
• Implement logging and observability
• Plan for scalability and failover