The Model Context Protocol (MCP) is an open standard developed to seamlessly connect AI assistants with various data sources, such as content repositories, business tools, and development environments. Introduced by Anthropic in late 2024, MCP aims to provide a universal interface between large language model (LLM) applications and external data or functionalities, simplifying integration processes and enhancing AI capabilities.
Why do you need MCP
Traditionally, integrating AI models with different data sources required bespoke connectors, leading to fragmented and complex systems. MCP addresses this challenge by providing a standardized method for establishing secure, two-way connections between AI applications and external data repositories. This standardization simplifies the development process, allowing AI systems to retrieve and utilize relevant information more efficiently.
This standardization simplifies the development process, allowing AI systems to retrieve and utilize relevant information more efficiently.
The MCP is an open standard designed to simplify how applications share context with large language models (LLMs). Think of MCP as a "USB-C port" for AI - just like USB-C lets you easily connect your phone or laptop to all kinds of gadgets, MCP makes it effortless for AI models to connect to various data sources and tools. This means less hassle, smoother integration, and better, smarter AI experiences.
MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:
- A growing list of pre-built integrations that your LLM can directly plug into
- The flexibility to switch between LLM providers and vendors
- Best practices for securing your data within your infrastructure
General architecture
At its core, MCP follows a client-server architecture where a host application can connect to multiple servers:
- MCP Hosts: programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP.
❗️ Because servers are locally run, MCP currently only supports desktop hosts. Remote hosts are in active development.
- MCP Clients: protocol clients that maintain 1-to-1 connections with servers.
- MCP Servers: lightweight programs that each expose specific capabilities through the standardized Model Context Protocol.
- Local Data Sources: your computer's files, databases, and services that MCP servers can securely access.
- Remote Services: external systems available over the internet (e.g., through APIs) that MCP servers can connect to.
Use Cases
The potential of MCP servers is vast. They can be used for a variety of purposes:
Web Services and API Integration:
- Monitor GitHub repositories for new issues
- Post updates to Twitter based on specific triggers
- Retrieve real-time weather data for location-based services
Browser Automation:
- Automate web application testing
- Scrape e-commerce sites for price comparisons
- Generate screenshots for website monitoring
Database Queries:
- Generate weekly sales reports
- Analyze customer behavior patterns
- Create real-time dashboards for business metrics
Project and Task Management:
- Automate Jira ticket creation based on code commits
- Generate weekly progress reports
- Create task dependencies based on project requirements
Codebase Documentation:
- Generate API documentation from code comments
- Create architecture diagrams from code structure
- Maintain up-to-date README files
Getting Started
Choose the right approach for your needs:
- Use Existing Servers: start with pre-built MCP servers from GitHub repositories.
- Customize Existing Servers: modify existing servers to fit your specific requirements.
- Build from Scratch: create completely custom servers for unique use cases.
Today, I will share with you the step-by-step guidance to start using MCP Server in your local machine. We will use Claude Desktop as a Client (Host) and extend it so that it can read from your computer's file system, write new files, move files, and even search files.
(You will be able to find more popular MCP Clients and Servers in the next section).
1️⃣ Download Claude Desktop App.
Follow the installation instructions and sign-in after it is installed.
2️⃣ Add the Filesystem MCP Server.
- Get started by opening up the Claude menu on your computer and select "Settings…":
- Click on "Developer" in the lefthand bar of the Settings pane, and then click on "Edit Config":
"Edit Config" will create a configuration file at:
- Windows:
%APPDATA%\Claude\claude_desktop_config.json
if you don't already have one, and will display the file in your file system.
- Open up the configuration file
claude_desktop_config.json in any text editor. Replace the file contents with this:
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"C:\\Users\\ivanv\\Downloads"
]
}
}
}
NOTE: make sure to replace ivanv with your computer's username.
The paths should point to valid directories that you want Claude to be able to access and modify. It's set up to work for Downloads, but you can add more paths as well.
How does the configuration file work?
This configuration file tells Claude for Desktop which MCP servers to start up every time you start the application. In this case, we have added one server called "filesystem" that will use the Node npx command to install and run @modelcontextprotocol/server-filesystem. This server will let you access your file system in Claude for Desktop.
- You will also need Node.js on your computer for this to run properly. Follow the installation instructions.
To verify you have Node installed, open the command line on your computer.
- On Windows, press Windows+R, type "cmd", and press Enter
Once in the command line, verify you have Node installed by entering in the following command:
node --version
If you get an error saying "command not found" or "node is not recognized", it means the Node it not installed properly. Otherwise, it will show you the installed version of Node.js:
C:\Users\ivanv>node --version
v22.14.0
3️⃣ Restart Claude.
After updating your configuration file, you need to restart Claude for Desktop. Upon restarting, you should see a hammer icon in the bottom right corner of the input box:
After clicking on the hammer icon, you should see the tools that come with the Filesystem MCP Server:
4️⃣ Try it out!
You can now talk to Claude and ask it about your filesystem. It should know when to call the relevant tools.
Let's start from making sure what directories are available to Claude by simply asking it about that. As needed, Claude will call the relevant tools and seek your approval before taking an action:
Here is its answer:
❗️ TROUBLESHOOTING
❌ Server not showing up in Claude / hammer icon missing
- Restart Claude for Desktop completely (you could use Task Manager)
- Check your
claude_desktop_config.json file syntax
- Make sure the file paths included in
claude_desktop_config.json are valid and that they are absolute and not relative
- Look at logs (next point below) to see why the server is not connecting
- In your command line, try manually running the server (replacing username as you did in
claude_desktop_config.json) to see if you get any errors:
npx -y @modelcontextprotocol/server-filesystem C:\Users\{username}\Downloads
❌ How to get logs
Claude logging related to MCP is written to log files in:
- Windows:
%APPDATA%\Claude\logs
mcp.log will contain general logging about MCP connections and connection failures.- Files named
mcp-server-SERVERNAME.log will contain error (stderr) logging from the named server.
You can run the following command to list recent logs and follow along with any new ones (on Windows, it will only show recent logs):
type "%APPDATA%\Claude\logs\mcp*.log"
❌ If Claude attempts to use the tools but they fail:
- Check Claude's logs for errors
- Verify your server builds and runs without errors
- Try restarting Claude for Desktop (you could use Task Manager)
Popular MCP Clients
- Claude Desktop App: full support for all MCP features.
- Cursor: great AI IDE (fork from VS Code), supports various tools.
- Windsurf Editor: supports tools with AI Flow for collaborative development.
🔗 You can find more by this link: Example Clients - Model Context Protocol
Popular MCP Servers
Several MCP servers have been developed to facilitate integration with commonly used platforms:
🔗 More references you can find by this link: modelcontextprotocol/servers: Model Context Protocol Servers
How does it work
MCP follows a client-server architecture where:
- Clients maintain 1-to-1 connections with servers, inside the host application. Host applications are LLM applications (like Claude Desktop or IDEs) that initiate connections.
- Servers provide context, tools, and prompts to clients.
Core components
- Protocol Layer - handles message framing, request/response linking, and high-level communication patterns.
- Transport Layer - handles the actual communication between clients and servers. MCP supports multiple JSON-RPC transport mechanisms: Stdio transport (local i/o) and HTTP with SSE transport (client-server).
- Message types:
MCP has these main types of messages:
- Requests expect a response from the other side:
interface Request {
method: string;
params?: { ... };
}
- Results are successful responses to requests:
interface Result {
[key: string]: unknown;
}
- Errors indicate that a request failed:
interface Error {
code: number;
message: string;
data?: unknown;
}
- Notifications are one-way messages that don't expect a response:
interface Notification {
method: string;
params?: { ... };
}
Connection Lifecycle
1️⃣ Initialization
- Client sends initialize request with protocol version and capabilities.
- Server responds with its protocol version and capabilities.
- Client sends initialized notification as acknowledgment.
- Normal message exchange begins.
2️⃣ Message exchange
After initialization, the following patterns are supported:
- Request-Response: client or server sends requests, the other responds
- Notifications: either party sends one-way messages
3️⃣ Termination
Either party can terminate the connection:
- Clean shutdown via
close()
- Transport disconnection
- Error conditions*
* MCP defines these standard error codes:
enum ErrorCode {
// Standard JSON-RPC error codes
ParseError = -32700,
InvalidRequest = -32600,
MethodNotFound = -32601,
InvalidParams = -32602,
InternalError = -32603
}
SDKs and applications can define their own error codes above -32000.
Errors are propagated through:
- Error responses to requests
- Error events on transports
- Protocol-level error handlers
Best practices
Transport selection
Local communication
- Use stdio transport for local processes
- Efficient for same-machine communication
- Simple process management
Remote communication
- Use SSE for scenarios requiring HTTP compatibility
- Consider security implications including authentication and authorization
Message handling
Request processing
- Validate inputs thoroughly
- Use type-safe schemas
- Handle errors gracefully
- Implement timeouts
Progress reporting
- Use progress tokens for long operations
- Report progress incrementally
- Include total progress when known
Error management
- Use appropriate error codes
- Include helpful error messages
- Clean up resources on errors
Security considerations
Transport security
- Use TLS for remote connections
- Validate connection origins
- Implement authentication when needed
Message validation
- Validate all incoming messages
- Sanitize inputs
- Check message size limits
- Verify JSON-RPC format
Resource protection
- Implement access controls
- Validate resource paths
- Monitor resource usage
- Rate limit requests
Error handling
- Don't leak sensitive information
- Log security-relevant errors
- Implement proper cleanup
- Handle DoS scenarios
Debugging and monitoring
Logging
- Log protocol events
- Track message flow
- Monitor performance
- Record errors
Diagnostics
- Implement health checks
- Monitor connection state
- Track resource usage
- Profile performance
Testing
- Test different transports
- Verify error handling
- Check edge cases
- Load test servers
Future of MCP
MCP represents a significant advancement in AI integration, offering a unified and efficient approach to connecting AI systems with diverse data sources. As more organizations adopt MCP, we can anticipate a more connected and efficient AI ecosystem, reducing the need for custom integrations and promoting interoperability. This standardization could improve the performance and scalability of AI applications across various industries, paving the way for more intelligent and practical AI solutions.
🔗 You can find the MCP Roadmap for 2025 here: Roadmap - Model Context Protocol
In conclusion, MCP serves as a pivotal development in the AI landscape, simplifying integrations and enhancing the capabilities of AI assistants. By adopting MCP, developers and organizations can create more responsive, context-aware, and efficient AI applications, ultimately benefiting end-users with more seamless and intelligent interactions.
🔗 More information is there: Introduction - Model Context Protocol