Model Context Protocol (MCP) is how AI connects to external tools and data. What it is, how it works, and why it matters for AI development.
MCP Servers Explained: The New Standard for AI Tool Integration
Claude can now read your files, query your database, search the web, and use any tool you give it access to. How?
MCP: Model Context Protocol.
Think of it as USB for AI. A standard way to connect AI models to external tools and data sources. This technology powers the AI agent revolution, enabling autonomous AI systems to interact with your tools and data.
Here's what you need to know.
The Problem MCP Solves
AI models are trained on static data. They don't know:
- What's in your files right now
- What's in your database
- Current information from the web
- Your specific business systems
Before MCP, connecting AI to external tools required:
- Custom code for each integration
- Different approaches for different models
- Fragile implementations that break
- Security nightmares
MCP standardizes this. One protocol, any tool, any AI that supports it.
How MCP Works
The Architecture
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ AI Model │────▶│ MCP Client │────▶│ MCP Server │────▶ External Tool
│ (Claude) │◀────│ (Protocol) │◀────│ (Your code) │◀──── (Database, API, etc)
└─────────────┘ └─────────────┘ └─────────────┘
AI Model: Claude or another MCP-compatible model
MCP Client: Built into the AI application (Claude Desktop, etc.)
MCP Server: A small program you run that exposes tools to the AI
External Tool: Whatever you're connecting—files, databases, APIs
The Flow
- You ask Claude something that requires external data
- Claude recognizes it needs a tool
- Claude calls the appropriate MCP server
- The server fetches/processes the data
- Server returns results to Claude
- Claude incorporates the data into its response
All transparent to you. You just ask; Claude handles the rest.
What MCP Servers Can Do
Resources
Expose data sources for Claude to read:
- Files on your computer
- Database contents
- API responses
- Live web pages
Tools
Give Claude actions it can take:
- Run database queries
- Send API requests
- Execute code
- Modify files
Prompts
Provide pre-built prompts for specific tasks:
- Analysis templates
- Workflow automations
- Domain-specific instructions
Common MCP Servers
Filesystem
What it does: Read and write files on your computer.
Use case: "Summarize all the PDFs in my Downloads folder."
Database (PostgreSQL, SQLite, etc.)
What it does: Query and modify databases.
Use case: "Show me all customers who signed up last month."
GitHub
What it does: Read repos, create issues, manage PRs.
Use case: "What are the open issues in my project?"
Slack
What it does: Read messages, post updates.
Use case: "Summarize today's discussion in #engineering."
Google Drive
What it does: Access your Drive files.
Use case: "Find the Q3 report and pull out the revenue numbers."
Web Fetch/Search
What it does: Browse the web, search for information.
Use case: "What's the current price of Bitcoin?"
Memory
What it does: Persistent memory across conversations.
Use case: Remember project context, preferences, past decisions.
Setting Up MCP (Claude Desktop)
Step 1: Install Claude Desktop
Download from Anthropic. MCP is built into the desktop app.
Step 2: Configure MCP Servers
Edit the Claude configuration file:
Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Step 3: Add Server Configuration
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/you/Documents"]
}
}
}
Step 4: Restart Claude
The server appears in Claude's available tools.
Step 5: Use It
Ask Claude something that requires the tool. It will automatically use the MCP server.
Building Your Own MCP Server
You can create custom servers for your specific needs.
Basic Structure
An MCP server needs to:
- Connect via stdio (standard input/output)
- Implement the MCP protocol
- Expose tools/resources
- Handle requests from Claude
Example: Simple Tool Server (Python)
from mcp import Server, Tool
server = Server("my-tools")
@server.tool()
def get_weather(city: str) -> str:
"""Get current weather for a city."""
# Your implementation
return f"Weather in {city}: 72°F, sunny"
@server.tool()
def search_database(query: str) -> str:
"""Search the internal database."""
# Your implementation
return "Results: ..."
if __name__ == "__main__":
server.run()
Example: Resource Server
from mcp import Server, Resource
server = Server("my-data")
@server.resource("sales://monthly")
def monthly_sales() -> str:
"""Get monthly sales data."""
return fetch_sales_data()
@server.resource("customers://{id}")
def customer_info(id: str) -> str:
"""Get customer information by ID."""
return fetch_customer(id)
Security Considerations
What MCP Servers Can Access
By default, MCP servers can only access what you explicitly configure. But understand:
- A filesystem server can read/write permitted directories
- A database server can execute queries
- A web server can make HTTP requests
Best Practices
Principle of Least Privilege
Only give servers access to what's necessary. Don't share your entire filesystem when you only need one folder.
Review Server Code
Before running third-party MCP servers, review the code. They run on your machine with the permissions you grant.
Use Official Servers When Possible
Anthropic and the community maintain official servers that are reviewed for security.
Separate Sensitive Data
Don't expose production databases or sensitive systems to AI without careful consideration.
Monitor Usage
Log what your MCP servers do. Know what Claude is accessing.
MCP vs. Other Approaches
MCP vs. Function Calling
Function calling (OpenAI, Google) lets models call predefined functions. MCP is similar but:
- Standardized across models
- Supports resources (read-only data) not just tools
- Has a defined protocol for discovery
The choice between different AI models (Claude vs GPT-4 vs Gemini) matters less when MCP provides a standard integration layer.
MCP vs. Plugins (ChatGPT)
ChatGPT plugins were hosted remotely. MCP servers run locally, giving you more control and security.
MCP vs. Custom Integrations
Before MCP, you'd build custom code for each AI-tool connection. MCP standardizes this, making integrations portable and reusable.
The MCP Ecosystem
Official Servers
Anthropic maintains servers for common use cases:
- Filesystem
- Git
- Fetch (web requests)
- Memory
Community Servers
Growing list of community-built servers:
- Database connectors (Postgres, MySQL, MongoDB)
- Service integrations (Slack, GitHub, Linear)
- Custom tools for specific industries
Server Directory
Check the MCP repository for available servers:
github.com/anthropics/model-context-protocol
Who Should Care About MCP
Developers
MCP dramatically simplifies AI integration. Instead of building custom bridges, use standardized servers. If you're using AI coding assistants, MCP can extend their capabilities significantly.
IT/Operations
Understanding MCP is essential as AI tools proliferate. You'll be managing what servers are deployed and what they access.
Business Users
You don't need to understand the technical details, but knowing MCP exists helps you understand what's possible with AI tools.
AI Product Builders
MCP is becoming standard. Products that support MCP can leverage the entire ecosystem of servers.
The Future of MCP
MCP is new (late 2024) but already becoming standard. Expect:
- More AI models supporting MCP
- Richer ecosystem of servers
- Enterprise MCP management tools
- Better security and auditing
- Integration into mainstream software
Frequently Asked Questions
What is the Model Context Protocol (MCP)?
MCP is an open standard created by Anthropic that allows AI models to connect to external tools and data sources. It works like USB for AI—providing a standardized way for models like Claude to access files, databases, APIs, and other tools without requiring custom code for each integration.
How do I set up MCP servers with Claude?
Install Claude Desktop, then edit your configuration file (in Application Support folder on Mac or AppData on Windows) to add MCP server definitions. Each server specifies what tool or data source Claude can access, such as filesystem access or database queries. After restarting Claude, the servers appear as available tools.
Are MCP servers secure to use?
MCP servers only access what you explicitly configure, but they run with the permissions you grant. Follow security best practices: only give servers necessary access, review third-party server code before running, use official servers when possible, and avoid exposing sensitive production systems without careful consideration.
Can I build my own custom MCP servers?
Yes, you can create custom MCP servers for your specific needs. Servers can be built in Python or other languages, need to implement the MCP protocol, and must expose tools or resources that Claude can use. Basic examples include weather APIs, database queries, or custom business logic.
How is MCP different from ChatGPT plugins or function calling?
MCP is a standardized protocol that works across AI models, runs locally for better security and control, and supports both tools (actions) and resources (read-only data). ChatGPT plugins were remote and proprietary, while function calling requires custom implementation for each model.
Who should care about MCP servers?
Developers benefit from simplified AI integration, IT teams need to understand MCP for managing AI tool deployments, business users should know what's possible with AI tools, and AI product builders should adopt MCP as it becomes the standard for tool integration.
Getting Started
- Install Claude Desktop if you haven't
- Add the filesystem server (most useful starting point)
- Try it: Ask Claude about files on your computer
- Explore other servers as needs arise
- Consider building custom servers for your specific tools
MCP turns AI from "smart chatbot" to "capable assistant with access to your world."
That's a fundamental shift. And it's just getting started.
Need help integrating AI tools into your business workflows? Cedar Operations designs custom AI solutions. Let's discuss your needs →
Related reading:
CEDAR OPERATIONS