The Model Context Protocol (MCP) is an open standard developed by Anthropic and introduced in mid-2024. An MCP server functions to let AI models securely and efficiently interact with external tools, data sources, and services (examples being those mentioned on the cover image). This protocol is model-agnostic, meaning it can work with various AI models such as Claude, GPT, and other open-source LLMs and platforms, allowing for a flexible ecosystem. Early adopters, including Block (Square), Zed, Replit, Codeium, and Bind AI, have integrated MCP to enhance AI capabilities, particularly in making models context-aware for tasks like coding or enterprise IT. Not to mention adding advanced integration capabilities like Bind AI’s native GitHub integration.
MCP addresses the limitation of AI models being confined to their training data by allowing real-time data access and action execution, bridging the gap between AI and real-world systems. This is particularly relevant in 2025, with growing adoption in software development for AI co-pilots. But is it possible to create your own MCP? Well, yes. Let’s learn now.
Understanding MCP Servers
An MCP server is a software application that implements the MCP specification, acting as a bridge between AI models (hosts) and external resources such as databases, APIs, file systems, and more (servers). It provides “tools” or functionalities that AI models can invoke, ranging from basic operations like file reading to complex tasks like database queries. For instance, a database MCP server (QDrant DB, for example) might offer tools to create tables or run SQL queries, while a file system server could handle directory listings or file operations.
The role of an MCP server is critical in the MCP ecosystem, as it allows AI to perform tasks beyond its static training data. Examples of existing servers include those for GitHub (repository management), Postgres (database operations), and Puppeteer (web scraping), as seen in community repositories like the MCP Servers GitHub Repository.
How MCP Servers Operate
MCP follows a client-host-server architecture:
- MCP Host: The AI application or assistant (e.g., Claude Desktop, Cursor, Bind AI) hosting the language model, which uses external tools via MCP servers.
- MCP Server: The server (e.g., Google Drive, GitHub, GitLab, AWS S3, Llama Cloud, etc.) provides tools and resources, communicating with the host to execute requests.
Communication is standardized, ensuring interoperability across different AI models and servers. Security is paramount, with servers able to implement authentication (e.g., OAuth 2.0) and authorization to restrict access, protecting sensitive data during transit.
How To Create An MCP Server: A Step-by-Step Guide
Creating an MCP server involves several steps, with flexibility based on programming language and use case. Below is a detailed process:
1. Choosing a Programming Language
MCP SDKs are available for Python, C#, and TypeScript, among others. Python is ideal for rapid prototyping, C# for robust enterprise applications, and TypeScript for web-based integrations (the most common). For instance, the Python SDK is accessible via pip install mcp, while C# uses the NuGet package ModelContextProtocol.
2. Installing the MCP SDK
- Python: Use pip install mcp or uv add “mcp[cli]” for a faster Rust-based implementation.
- C#: Add the package with dotnet add package ModelContextProtocol –prerelease, noting it’s in preview as of April 2025.
- TypeScript: Refer to TypeScript SDK Documentation for setup.
3. Setting Up Your Project
Create a new project:
- For Python: Start with a script like server.py.
- For C#: Use dotnet new console -n MyFirstMCP to create a console application.
4. Defining Tools
Tools are functions annotated with SDK attributes or decorators, representing what the server can do. For example:
- In Python:
python
from mcp.server.fastmcp import FastMCP mcp = FastMCP(“my_mcp_server”) @mcp.tool def get_latest_emails():
# Implementation pass
- In C#:
csharp
[McpServerToolType] public static class EchoTool { [McpServerTool, Description(“Echoes the message back to the client.”)] public static string Echo(string message) => $”Hello from C#: {message}”; }
Each tool should be documented for AI models to understand its purpose and parameters.
5. Configuring the Server
Set up the server to listen for requests:
- In Python: Use FastMCP for server setup.
- In C#: Use Host.CreateApplicationBuilder with AddMcpServer().WithStdioServerTransport().WithToolsFromAssembly():
csharp
var builder = Host.CreateApplicationBuilder(args); builder.Services.AddMcpServer().WithStdioServerTransport().WithToolsFromAssembly(); await builder.Build().RunAsync();
6. Handling Authentication
For services requiring access (e.g., Gmail), implement authentication:
- For Gmail/Calendar: Set up OAuth 2.0, following Google OAuth Setup, downloading credentials.json, and creating token.pickle.
- For other services: Use API keys or tokens as needed.
7. Running and Configuring the Server
Start the server and add it to your AI client’s configuration:
- For Claude Desktop: Add to claude_desktop_config.json, e.g.:
json
“gmail_mcp_server”: {“command”: “uv”, “args”: [“–directory”, “path/to/server”, “run”, “server.py”]}
- For Cursor: Add to .cursor/mcp.json, e.g.:
json
{ “mcpServers”: { “my_mcp_server”: { “command”: “python”, “args”: [“server.py”] } } }
8. Testing and Troubleshooting
Test with an MCP-compatible client (e.g., Claude Desktop, Cursor). Common issues include:
- npm error enoent: Uninstall Node.js, reinstall via Node Version Manager.
- Error: spawn uv ENOENT: Install uv from UV Getting Started.
- JSON syntax errors: Verify configuration files and check logs (e.g., ~/Library/ApplicationSupport/Claude/logs on macOS).
Practical Example: Building a Gmail and Google Calendar MCP Server in Python
Let’s build a server for Gmail and Google Calendar access:
- Prerequisites: Install pip install mcp and set up OAuth 2.0 for Gmail/Calendar API, following Google OAuth Setup.
- Code:
python
from mcp.server.fastmcp import FastMCP import gmail_api # Hypothetical module import gcalendar_api # Hypothetical module mcp = FastMCP(“gmail_mcp_server”)@mcp.resource def get_latest_emails(): return gmail_api.get_latest_emails()@mcp.tool def search_specific_email(query): return gmail_api.search_emails(query)@mcp.tool def get_email_content(email_id): return gmail_api.get_email_content(email_id)@mcp.resource def search_events(query): return gcalendar_api.search_events(query)@mcp.tool def create_new_event(event_details): return gcalendar_api.create_event(event_details)
- Run: Save as server.py, use mcp install server.py, and configure in your AI client.
This example, detailed in Dev Shorts Article, shows 3 Gmail tools and 2 GCalendar tools, with full code at MCP Server GitHub.
Integrating with Specific Services
Many services have MCP servers. For example:
- Supabase: Offers over 20 tools, including table design and data fetching. Set up with a personal access token in .cursor/mcp.json:
json
{ “mcpServers”: { “supabase”: { “command”: “npx”, “args”: [“-y”, “@supabase/mcp-server-supabase@latest”, “–access-token”, “<your-pat>”] } } }
Details at Supabase MCP Blog, with tools listed at Supabase MCP Tools.
Other integrations include GitHub for repository management and Puppeteer for web scraping, as seen in MCP Servers Collection.
Advanced Configurations and Best Practices
For advanced setups:
- C# Example: Build with dotnet new console, add NuGet packages, and define tools as shown earlier. Publish as a container with:
xml
<PropertyGroup> <EnableSdkContainerSupport>true</EnableSdkContainerSupport> <ContainerRepository>jamesmontemagno/monkeymcp</ContainerRepository> <ContainerFamily>alpine</ContainerFamily> <RuntimeIdentifiers>linux-x64;linux-arm64</RuntimeIdentifiers> </PropertyGroup>
Publish with dotnet publish /t:PublishContainer -p ContainerRegistry=docker.io, detailed in .NET MCP Blog.
Best Practices
- Security: Implement OAuth 2.0 or API keys, ensuring data protection.
- Performance: Optimize tools for efficiency, especially for database queries.
- Error Handling: Gracefully handle errors, providing feedback to AI models.
- Documentation: Use descriptions in tool definitions for clarity, e.g., [Description(“Echoes the message back to the client.”)] in C#.
The Bottom Line
Creating an MCP server allows AI models to access external resources securely and efficiently. This improves their usefulness in applications like coding assistants or business systems. By following the steps provided, using SDKs, and following best practices, developers can build custom servers that meet specific needs.