In the rapidly evolving landscape of artificial intelligence, one of the most significant challenges has been creating standardized ways for AI models to interact with external data sources and tools. Enter the Model Context Protocol (MCP) – an innovation fundamentally changing how AI models connect to the digital world around them.
Much like how USB revolutionized hardware connectivity by providing a universal standard that allowed any compatible device to connect to any compatible computer, MCP is doing the same for AI models. Before USB, connecting peripherals to computers was complex, with numerous proprietary connectors and protocols. Similarly, before MCP, integrating AI models with external tools and data sources required custom implementations for each integration point.
MCP servers are the intermediary layer that standardizes these connections, allowing Large Language Models (LLMs) like Claude to seamlessly access various data sources and tools through a consistent interface. This standardization transforms how developers build AI applications, making it easier to create powerful, context-aware AI systems that can interact with the world in meaningful ways.
In this article, we’ll explore MCP servers, the company that created them, the different types available, and a practical implementation example to demonstrate their power and flexibility. By the end, you’ll understand why MCP servers truly are the “Universal USB for AI Models” and how they’re shaping the future of AI integration.
The Company Behind MCP: Anthropic
The Model Context Protocol (MCP) was developed by Anthropic, an AI safety company founded in 2021 by former members of OpenAI, including Dario Amodei (CEO) and Daniela Amodei (President). Anthropic was established with a mission to develop AI systems that are safe, beneficial, and honest.
Anthropic has gained significant recognition in the AI industry for developing Claude, a conversational AI assistant designed to be helpful, harmless, and honest. The company has raised substantial funding to support its research and development efforts, including investments from Google, Spark Capital, and other major tech investors.
The development of MCP represents Anthropic’s commitment to creating more capable and safer AI systems. By standardizing how AI models interact with external tools and data sources, MCP addresses several key challenges in AI development:
- Safety and control: MCP provides a structured way for AI models to access external capabilities while maintaining appropriate safeguards.
- Interoperability: It creates a common language for AI models to communicate with various tools and services.
- Developer efficiency: It simplifies the process of building AI applications by providing a consistent interface for integrations.
- Flexibility: It allows AI models to be easily connected to new tools and data sources as needs evolve.
Anthropic announced MCP as part of its strategy to make Claude more capable while maintaining its commitment to safety. The protocol has since been open-sourced, allowing the broader developer community to contribute to its development and create a growing ecosystem of MCP servers.
What is MCP?
The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). At its core, MCP follows a client-server architecture that enables seamless communication between AI applications and various data sources or tools.
Core Architecture
MCP is built on a flexible, extensible architecture with several key components:
- Hosts: These are LLM applications like Claude Desktop or integrated development environments (IDEs) that initiate connections to access data through MCP.
- Clients: These protocol clients maintain one-to-one connections with servers inside the host application.
- Servers: These lightweight programs expose specific capabilities through the standardized Model Context Protocol, providing context, tools, and prompts to clients.
The protocol layer handles message framing, request/response linking, and high-level communication patterns, while the transport layer manages communication between clients and servers. MCP supports multiple transport mechanisms, including stdio transport for local processes and HTTP with Server-Sent Events (SSE) for web-based communications.
Capabilities
MCP servers can provide three main types of capabilities:
- Resources: File-like data clients can read, such as API responses or file contents.
- Tools: Functions that can be called by the LLM (with user approval), enabling the AI to perform specific actions or retrieve particular information.
- Prompts: Pre-written templates that help users accomplish specific tasks.
Benefits of MCP
The MCP approach offers several significant advantages:
- Standardization: As USB standardized hardware connections, MCP standardizes how AI models connect to external tools and data sources.
- Flexibility: Developers can switch between LLM providers and vendors without changing their integration code.
- Security: MCP implements best practices for securing data within your infrastructure.
- Extensibility: The growing ecosystem of pre-built integrations allows LLMs to plug into various services directly.
- Modularity: Each MCP server focuses on a specific capability, making the system more maintainable and easier to reason about.
Types of MCP Servers
The MCP ecosystem has grown rapidly, with numerous servers available for different purposes. These servers can be categorized in several ways:
By Function
Data Access Servers
These servers provide access to various data storage systems:
- Google Drive MCP Server: Enables file access and search capabilities for Google Drive.
- PostgreSQL MCP Server: Provides read-only database access with schema inspection.
- SingleStore MCP Server: Facilitates database interaction with table listing, schema queries, and SQL execution.
- Redis MCP Server: Allows interaction with Redis key-value stores.
- Sqlite MCP Server: Supports database interaction and business intelligence capabilities.
Search Servers
These servers enable AI models to search for information:
- Brave Search MCP Server: Provides web and local search using Brave’s Search API.
- DuckDuckGo Search MCP Server: Offers organic web search with a privacy-focused approach.
- Exa MCP Server: A search engine made specifically for AIs.
Development & Repository Servers
These servers facilitate code and repository management:
- GitHub MCP Server: Enables repository management, file operations, and GitHub API integration.
- GitLab MCP Server: Provides access to GitLab API for project management.
- Git MCP Server: Offers tools to read, search, and manipulate Git repositories.
- CircleCI MCP Server: Helps AI agents fix build failures.
Communication & Collaboration Servers
These servers enable interaction with communication platforms:
- Slack MCP Server: Provides channel management and messaging capabilities.
- Fibery MCP Server: Allows queries and entity operations in workspaces.
- Dart MCP Server: Facilitates task, doc, and project data interaction.
Infrastructure & Operations Servers
These servers manage infrastructure components:
- Docker MCP Server: Enables isolated code execution in containers.
- Cloudflare MCP Server: Allows deployment, configuration, and interrogation of resources on Cloudflare.
- Heroku MCP Server: Facilitates interaction with the Heroku Platform for managing apps and services.
- E2B MCP Server: Runs code in secure sandboxes.
Content & Media Servers
These servers handle various types of content:
- EverArt MCP Server: Provides AI image generation using various models.
- Fetch MCP Server: Enables web content fetching and conversion for efficient LLM usage.
- Filesystem MCP Server: Offers secure file operations with configurable access controls.
Location & Mapping Servers
These servers provide location-based services:
- Google Maps MCP Server: Offers location services, directions, and place details.
AI Enhancement Servers
These servers enhance AI capabilities:
- Vectorize MCP Server: Provides vector searches, deep research report generation, and text extraction.
- Memory MCP Server: Implements a knowledge graph-based persistent memory system.
- Sequential Thinking MCP Server: Facilitates dynamic problem-solving through thought sequences.
- Chroma MCP Server: Offers embeddings, vector search, document storage, and full-text search.
By Integration Type
MCP servers can also be categorized by how they integrate with systems:
- Local System Integrations: These connect to resources on the local machine, like the Filesystem MCP Server.
- Cloud Service Integrations connect to cloud-based services like the GitHub MCP Server or Google Drive MCP Server.
- API-Based Integrations: These leverage external APIs, like the Brave Search MCP Server or Google Maps MCP Server.
- Database Integrations: These connect specifically to database systems, such as the PostgreSQL MCP Server or Redis MCP Server.
By Security & Privacy Focus
Some MCP servers place particular emphasis on security and privacy:
- High Privacy Focus: Servers like the DuckDuckGo Search MCP Server prioritize user privacy.
- Enterprise Security: Servers like the Cloudflare MCP Server or GitHub MCP Server include robust authentication and security features.
The diversity of available MCP servers demonstrates the protocol’s versatility and ability to connect AI models to virtually any data source or tool, much like how USB connects computers to a vast array of peripherals.
For more information, click here.
Step-by-Step Implementation Example
To demonstrate how MCP works in practice, let’s create a simple weather MCP server that provides weather forecasts and alerts to LLMs. This example will show how MCP servers act as a “Universal USB” for AI models by providing standardized access to external data and tools.
Prerequisites
- Python 3.10 or higher
- Familiarity with Python programming
- Basic understanding of LLMs like Claude
Step 1: Set Up Your Environment
First, let’s set up our development environment:
# Install uv package manager
curl -LsSf https://astral.sh/uv/install.sh | sh
# Create and set up our project
uv init weather
cd weather
# Create and activate virtual environment
uv venv
source .venv/bin/activate
# Install required packages
uv add "mcp[cli]" httpx
# Create our server file
touch weather.pyStep 2: Import Packages and Set Up the MCP Instance
Open weather.py And add the following code:
from typing import Any
import httpx
from mcp.server.fastmcp import FastMCP
# Initialize FastMCP server
mcp = FastMCP("weather")
# Constants
NWS_API_BASE = "https://api.weather.gov"
USER_AGENT = "weather-app/1.0"The FastMCP Class uses Python type hints and docstrings to automatically generate tool definitions, making creating and maintaining MCP tools easy.
Step 3: Create Helper Functions
Next, let’s add helper functions for querying and formatting data from the National Weather Service API:
async def make_nws_request(url: str) -> dict[str, Any] | None:
"""Make a request to the NWS API with proper error handling."""
headers = {
"User-Agent": USER_AGENT,
"Accept": "application/geo+json"
}
async with httpx.AsyncClient() as client:
try:
response = await client.get(url, headers=headers, timeout=10)
response.raise_for_status()
return response.json()
except Exception:
return None
def format_alert(feature: dict) -> str:
"""Format an alert feature into a readable string."""
props = feature["properties"]
return f"""
Event: {props.get('event', 'Unknown')}
Area: {props.get('areaDesc', 'Unknown')}
Severity: {props.get('severity', 'Unknown')}
Description: {props.get('description', 'No description available')}
Instructions: {props.get('instruction', 'No specific instructions')}
"""Step 4: Implement Tool Execution
Now, let’s implement the actual tools that our MCP server will expose:
@mcp.tool()
async def get_alerts(state: str) -> str:
"""Get weather alerts for a US state.
Args:
state: Two-letter US state code (e.g. CA, NY)
"""
url = f"{NWS_API_BASE}/alerts/active/area/{state}"
data = await make_nws_request(url)
if not data or "features" not in data:
return "Unable to fetch alerts or no alerts found."
if not data["features"]:
return "No active alerts for this state."
alerts = [format_alert(feature) for feature in data["features"]]
return "\n---\n".join(alerts)
@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
"""Get weather forecast for a location.
Args:
latitude: Latitude of the location
longitude: Longitude of the location
"""
# First get the forecast grid endpoint
points_url = f"{NWS_API_BASE}/points/{latitude},{longitude}"
points_data = await make_nws_request(points_url)
if not points_data:
return "Unable to fetch forecast data for this location."
# Get the forecast URL from the points response
forecast_url = points_data["properties"]["forecast"]
forecast_data = await make_nws_request(forecast_url)
if not forecast_data:
return "Unable to fetch detailed forecast."
# Format the periods into a readable forecast
periods = forecast_data["properties"]["periods"]
forecasts = []
for period in periods[:5]: # Only show next 5 periods
forecast = f"""
{period['name']}:
Temperature: {period['temperature']}{period['temperatureUnit']}
Wind: {period['windSpeed']} {period['windDirection']}
Forecast: {period['detailedForecast']}
"""
forecasts.append(forecast)
return "\n---\n".join(forecasts)Step 5: Run the Server
Finally, let’s add the code to initialize and run the server:
if __name__ == "__main__":
# Initialize and run the server
mcp.run(transport='stdio')Step 6: Test Your Server
Run your server to confirm everything’s working:
uv run weather.pyStep 7: Connect to an MCP Host (Claude for Desktop)
To use your server with Claude for Desktop:
- Install Claude for Desktop from the official website
- Configure Claude for Desktop by editing
~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": [
{
"name": "weather",
"command": "uv --directory /ABSOLUTE/PATH/TO/PARENT/FOLDER/weather run weather.py"
}
]
}- Restart Claude for Desktop
- Look for the hammer icon to confirm your tools are available
- Test with queries like:
- “What’s the weather in Sacramento?”
- “What are the weather alerts in Texas?”
How It Works
When you ask a question in Claude:
- The client sends your question to Claude
- Claude analyzes the available tools and decides which one(s) to use
- The client executes the chosen tool(s) through the MCP server
- The results are sent back to Claude
- Claude formulates a natural language response
- The response is displayed to you
This implementation example demonstrates the power and simplicity of MCP. With relatively little code, we’ve created a server that allows an AI model to access real-time weather data—something it couldn’t do on its own. The standardized interface means that any MCP-compatible AI model can use this server without modification, just as any USB-compatible computer can use a USB peripheral.
Examples of servers and implementations
This page showcases various Model Context Protocol (MCP) servers that demonstrate the protocol’s capabilities and versatility. These servers enable Large Language Models (LLMs) to access tools and data sources securely.
Reference implementations
These official reference servers demonstrate core MCP features and SDK usage:
Data and file systems
- Filesystem – Secure file operations with configurable access controls
- PostgreSQL – Read-only database access with schema inspection capabilities
- SQLite – Database interaction and business intelligence features
- Google Drive – File access and search capabilities for Google Drive
Development tools
- Git – Tools to read, search, and manipulate Git repositories
- GitHub – Repository management, file operations, and GitHub API integration
- GitLab – GitLab API integration enabling project management
- Sentry – Retrieving and analyzing issues from Sentry.io
Web and browser automation
- Brave Search – Web and local search using Brave’s Search API
- Fetch – Web content fetching and conversion optimized for LLM usage
- Puppeteer – Browser automation and web scraping capabilities
Productivity and communication
- Slack – Channel management and messaging capabilities
- Google Maps – Location services, directions, and place details
- Memory – Knowledge graph-based persistent memory system
AI and specialized tools
- EverArt – AI image generation using various models
- Sequential Thinking – Dynamic problem-solving through thought sequences
- AWS KB Retrieval – Retrieval from AWS Knowledge Base using Bedrock Agent Runtime
Official integrations
These MCP servers are maintained by companies for their platforms:
- Axiom – Query and analyze logs, traces, and event data using natural language
- Browserbase – Automate browser interactions in the cloud
- Cloudflare – Deploy and manage resources on the Cloudflare developer platform
- E2B – Execute code in secure cloud sandboxes
- Neon – Interact with the Neon serverless Postgres platform
- Obsidian Markdown Notes – Read and search through Markdown notes in Obsidian vaults
- Qdrant – Implement semantic memory using the Qdrant vector search engine
- Raygun – Access crash reporting and monitoring data
- Search1API – Unified API for search, crawling, and sitemaps
- Stripe – Interact with the Stripe API
- Tinybird – Interface with the Tinybird serverless ClickHouse platform
- Weaviate – Enable Agentic RAG through your Weaviate collection(s)
Community highlights
A growing ecosystem of community-developed servers extends MCP’s capabilities:
- Docker – Manage containers, images, volumes, and networks
- Kubernetes – Manage pods, deployments, and services
- Linear – Project management and issue tracking
- Snowflake – Interact with Snowflake databases
- Spotify – Control Spotify playback and manage playlists
- Todoist – Task management integration
Note: Community servers are untested and should be used at your own risk. They are not affiliated with or endorsed by Anthropic.
For a complete list of community servers, visit the MCP Servers Repository.
MCP on Visual Studio Code
Visual Studio Code (VS Code) has embraced the MCP to enable AI models to interact seamlessly with external tools and services through a unified interface, allowing for more dynamic and context-aware coding experiences. In VS Code, MCP support is integrated into Copilot’s agent mode, permitting users to connect to various MCP-compatible servers. These servers can perform file operations, database queries, or API calls in response to natural language prompts. For instance, developers can configure MCP servers like @modelcontextprotocol/server-filesystem or @modelcontextprotocol/server-postgres. This will allow Copilot to read from or write to the file system and interact with PostgreSQL databases directly from the editor. This integration streamlines workflows by reducing the need for manual context switching and enables AI assistants to execute complex tasks within the development environment. As MCP continues to evolve, it promises to further bridge the gap between AI models and practical software tools, fostering a more efficient and intelligent coding ecosystem.
MCP on N8N
N8N is an open-source, low-code workflow automation tool that enables users to connect various applications and services to automate tasks seamlessly. With its intuitive interface and extensive integration capabilities, n8n empowers users to design complex workflows without extensive coding knowledge.
A significant advancement in n8n’s functionality is the integration of MCP. Within N8N, the MCP Client Tool node allows AI agents to interact with external MCP servers, enabling them to discover and utilize tools such as web search engines or custom APIs. Conversely, the MCP Server Trigger node enables N8N to expose its tools and workflows to external AI agents, allowing for dynamic and scalable AI-driven automation. This bidirectional integration enhances the flexibility and power of n8n, making it a robust platform for building intelligent, context-aware workflows.
Conclusion
The Model Context Protocol (MCP) represents a significant advancement in how AI models interact with the world. By providing a standardized interface for connecting LLMs to external data sources and tools, MCP servers truly function as the “Universal USB for AI Models.”
Just as USB transformed hardware connectivity by creating a universal standard that simplified connections between devices, MCP is doing the same for AI models. It eliminates the need for custom integrations for each data source or tool, replacing them with a consistent, well-defined protocol that makes development more efficient and systems more maintainable.
The growing ecosystem of MCP servers covers a wide range of functionalities, from data access and search to development tools and AI enhancements. This diversity demonstrates the protocol’s versatility and potential to connect AI models to virtually any external system.
For developers, MCP offers several key benefits:
- Standardization: A consistent interface for all integrations.
- Modularity: Each server focuses on a specific capability, making systems easier to reason about.
- Security: Built-in best practices for securing data.
- Flexibility: Switching between different LLM providers without changing integration code.
- Extensibility: A growing ecosystem of pre-built integrations.
As AI evolves and becomes more integrated into our digital infrastructure, standards like MCP will become increasingly important. They enable the interoperability and flexibility needed for AI systems to reach their full potential while maintaining appropriate safeguards.
The future of AI is not just about more powerful models, but also about how those models connect to and interact with the world around them. MCP servers are paving the way for this future, serving as the universal connectors that bring AI’s capabilities to real-world data and systems.
In the same way USB transformed how we connect devices to computers, MCP is transforming how we connect AI models to the digital world – truly making it the “Universal USB for AI Models.”
That’s it for today!
Sources:
Anthropic Official MCP Documentation – https://docs.anthropic.com/mcp
Anthropic’s Original MCP Announcement (2024) – https://www.anthropic.com/blog/model-context-protocol
MCP GitHub Repository (Anthropic) – https://github.com/anthropics/MCP
Anthropic Claude Desktop Announcement – https://www.anthropic.com/blog/claude-desktop-with-mcp-support
Microsoft Copilot Studio MCP Integration Announcement – https://techcommunity.microsoft.com/copilot-studio-mcp-integration
JetBrains Kotlin SDK for MCP Announcement – https://blog.jetbrains.com/kotlin/2024/12/kotlin-sdk-mcp-support
MCP Community Connectors and Servers Repository – https://github.com/mcp-community/connectors
Cursor IDE MCP Integration (Cursor official blog) – https://cursor.sh/blog/cursor-ai-mcp-support
Open MCP SDK for Python – https://github.com/anthropics/mcp-python-sdk
Open MCP SDK for C# (.NET) – https://github.com/mcp-community/mcp-dotnet-sdk
Cloudflare Developer Platform MCP Server – https://developers.cloudflare.com/mcp
Stripe MCP Server Documentation – https://stripe.com/docs/developers/mcp
Neon Postgres MCP Server Documentation – https://neon.tech/docs/mcp
Weaviate MCP Server Documentation – https://weaviate.io/developers/mcp-integration
Microsoft’s Language Server Protocol (LSP) Inspiration for MCP – https://microsoft.github.io/language-server-protocol
Open Source MCP Servers Example (GitHub) – https://github.com/mcp-community/example-servers
MCP Registry and Discovery Roadmap – https://docs.anthropic.com/mcp/roadmap
Community MCP Connectors (Docker, Kubernetes, Spotify, etc.) – https://github.com/mcp-community/connectors
MCP Example Implementation (Weather Server Tutorial) – https://docs.anthropic.com/mcp/tutorials/weather-server
Open MCP Protocol Specification – https://github.com/anthropics/mcp-spec
