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What is the Model Context Protocol (MCP)?

· 5 min read
ToolBoost Team
ToolBoost Team
ToolBoost Engineering Team

If you've been working with AI assistants like Claude, ChatGPT, or other LLMs, you've probably noticed a limitation: they can't access external data or tools without special integrations. The Model Context Protocol (MCP) is here to change that.

The Problem: Isolated AI Assistants​

Modern AI assistants are incredibly powerful, but they're fundamentally limited. They can:

  • Generate text and code
  • Analyze information you provide
  • Answer questions based on training data

But they can't:

  • Access your files or databases
  • Query real-time APIs
  • Execute commands
  • Interact with external systems

This means you're constantly copying and pasting data between your tools and your AI assistant. It's inefficient and breaks your workflow.

Enter the Model Context Protocol​

MCP is an open standard created by Anthropic that solves this problem. Think of it as a universal adapter that lets AI assistants securely connect to external tools and data sources.

What Makes MCP Special?​

  1. Standardized Protocol: One protocol works with any MCP-compatible AI assistant
  2. Secure: Built-in authentication and permission controls
  3. Extensible: Easy to create new integrations
  4. Open Source: Community-driven development

How MCP Works​

At its core, MCP defines three key concepts:

1. Tools​

Tools are functions your AI can call. For example:

  • read_file(path) - Read a file from disk
  • query_database(sql) - Query a database
  • create_github_issue(title, body) - Create a GitHub issue

2. Resources​

Resources are data sources your AI can access:

  • Files on your computer
  • Database tables
  • API endpoints
  • Documents

3. Prompts​

Reusable prompt templates that provide context:

  • Project-specific workflows
  • Common tasks
  • Best practices

Real-World Example​

Let's say you're working on a web application and need to debug a database issue. With MCP:

  1. You: "Show me all users who signed up in the last 24 hours"
  2. AI (via MCP): Connects to your PostgreSQL database using the postgres-mcp server
  3. AI: Executes SELECT * FROM users WHERE created_at > NOW() - INTERVAL '24 hours'
  4. AI: Returns the results formatted nicely

No copying, no pasting, no context switching!

MCP Architecture​

┌─────────────────┐
│   AI Assistant  │  (Claude, GPT, etc.)
│  (MCP Client)   │
└────────┬────────┘
         │ MCP Protocol
         │ (HTTP/SSE)
         │
┌────────┴────────┐
│   MCP Server    │  (GitHub, Postgres, etc.)
│   (ToolBoost)   │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  External Tool  │  (GitHub API, Database, etc.)
└─────────────────┘

Why This Matters for Developers​

Before MCP​

You needed to:

  1. Build custom integrations for each AI platform
  2. Maintain separate codebases
  3. Handle authentication and security yourself
  4. Deploy and scale infrastructure

With MCP​

You can:

  1. Use any MCP server with any MCP-compatible client
  2. Leverage existing integrations from the community
  3. Focus on your application logic
  4. Let platforms like ToolBoost handle the infrastructure

The MCP Ecosystem​

The MCP ecosystem is growing rapidly:

Popular MCP Servers:

  • GitHub MCP - Manage repositories, issues, and PRs
  • Filesystem MCP - Read and write files
  • Postgres MCP - Query databases
  • Supabase MCP - Access Supabase projects
  • Memory MCP - Persistent storage for conversations

Supported Clients:

  • Claude Desktop - Anthropic's desktop app
  • Cursor - AI-powered code editor
  • Windsurf - Codeium's AI assistant
  • Zed - High-performance editor
  • Custom applications - Build your own!

Getting Started with MCP​

Option 1: Run Locally​

Install an MCP server and configure your AI client:

# Install a Filesystem MCP server
npx -y @modelcontextprotocol/server-filesystem /path/to/directory

Configure in Claude Desktop:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/me/Documents"]
    }
  }
}

Challenges:

  • Need to run servers locally
  • Manage dependencies and versions
  • Configure for each client
  • Handle updates manually

Deploy MCP servers to the cloud with one click:

  1. Browse the catalog at toolboost.dev/catalog
  2. Click "Deploy Server"
  3. Get your connection URL
  4. Add to your AI client
{
  "mcpServers": {
    "github": {
      "serverUrl": "https://toolboost.dev/server/github/github-mcp-server/mcp?api_key=YOUR_KEY"
    }
  }
}

Benefits:

  • No local installation required
  • Automatic updates
  • Works from anywhere
  • Managed infrastructure
  • Secure environment variable storage

Use Cases​

For Developers​

  • Code Review: "Analyze the GitHub PR #123 and suggest improvements"
  • Database Debugging: "Show me slow queries from the last hour"
  • File Management: "Refactor all components in the /src directory"

For Data Analysts​

  • Data Exploration: "What are the trends in user signups this month?"
  • Report Generation: "Create a summary of sales data from the database"
  • API Integration: "Fetch the latest metrics from our analytics API"

For Content Creators​

  • Research: "Search the web for recent articles about MCP"
  • File Organization: "Organize my blog posts by date"
  • Workflow Automation: "Save this draft to my content folder"

Security Considerations​

MCP is designed with security in mind:

  1. Authentication: API keys or OAuth for server access
  2. Permissions: Granular control over what tools can do
  3. Sandboxing: Servers run in isolated environments
  4. Audit Logs: Track all tool invocations

When using ToolBoost:

  • API keys are encrypted at rest
  • Environment variables are stored securely
  • All requests are logged for auditing

The Future of MCP​

MCP is just getting started. We're seeing:

  • More integrations: New MCP servers every week
  • Better tooling: Improved SDKs and developer tools
  • Enterprise adoption: Companies building internal MCPs
  • AI evolution: LLMs designed specifically for MCP

Conclusion​

The Model Context Protocol is transforming how we work with AI assistants. By providing a standardized way to connect AI to external tools and data, MCP enables workflows that were previously impossible.

Whether you're a developer building applications, a data analyst exploring data, or anyone working with AI, MCP opens up new possibilities.

Ready to try MCP? Check out:

Have questions about MCP or ToolBoost? Reach out to us at contact@toolboost.dev