Introduction

What is MCP and Its Background?¶

MCP, or Model Context Protocol, is an open standard that helps AI applications, especially large language models (LLMs), connect with external data sources and tools. Think of it like a universal adapter for AI, making it easier for systems like chatbots or coding assistants to access files, APIs, or databases without custom setups for each. It was introduced by Anthropic, a company focused on AI, around November 2024, to solve the problem of AI being isolated from data, which often limits its usefulness.

How to Use It and Why It Matters¶

You can use MCP with tools like Cursor, an AI-powered code editor, and Claude, an AI model by Anthropic, by setting up MCP servers within their applications. For example, in Claude Desktop, you edit a configuration file to add servers, while in Cursor, you go to the MCP settings to add new servers. This setup lets AI perform tasks like reading files or querying databases directly.

MCP is important because it breaks down data silos, making AI more connected and efficient. It allows developers to build smarter AI systems that scale better, which is especially helpful in fields like software development or data analysis. However, its adoption is still in early stages, with some controversy around how widely it's supported across different platforms.

List of Open-Source Servers and Building Your Own¶

There are several open-source MCP servers you can use, such as:

Python SDK (Model Context Protocol Python SDK)
ChatSum, for summarizing chat messages
Chroma, for semantic document search
ClaudePost, for Gmail management

To build your own MCP server, start by checking the official documentation at Model Context Protocol Introduction. It guides you through using SDKs in languages like Python or Java, defining what your server does, and testing it with clients like Claude Desktop. This process might require some coding knowledge, but it's designed to be accessible with the right resources.

Survey Note: Comprehensive Analysis of Model Context Protocol¶

This section provides a detailed exploration of the Model Context Protocol (MCP), covering its definition, origin, functionality, importance, available open-source servers, integration with Cursor and Claude, and a step-by-step guide for building your own server. The analysis is based on recent online resources, reflecting the state as of February 25, 2025, and aims to offer a professional, thorough overview for readers interested in AI integration.

Understanding MCP: Definition and Origin¶

MCP, or Model Context Protocol, is an open protocol designed to standardize how applications provide context to large language models (LLMs). It acts as a universal interface, likened to a USB-C port for AI, enabling seamless connections to data sources and tools. This standardization addresses the challenge of AI models being isolated from data, trapped behind information silos and legacy systems, as noted in Anthropic's introduction (Introducing the Model Context Protocol | Anthropic).

The protocol was introduced by Anthropic, PBC, on November 24, 2024, as an open-source initiative to simplify AI integrations. Its development was motivated by the need for a universal standard to replace fragmented, custom implementations, allowing developers to focus on building smarter, scalable AI systems. This origin is detailed in community discussions and official documentation, such as Getting Started: Model Context Protocol | Medium, highlighting its early adoption by companies like Block and Apollo.

Functionality: What MCP Does¶

MCP operates on a client-server architecture, where MCP hosts (e.g., Claude Desktop, IDEs, or AI tools) connect to MCP servers that expose specific capabilities. These servers can provide:

Prompts: Pre-defined templates guiding LLM interactions.
Resources: Structured data or content for additional context.
Tools: Executable functions for actions like fetching data or executing code.

This is outlined in the specification (Server Features – Model Context Protocol Specification), which details how servers enable rich interactions. For instance, MCP allows AI to access local files, query databases, or integrate with APIs, enhancing real-time data access and workflow automation. Its flexibility is evident in supporting multiple transports (e.g., stdio, sse) and a growing list of pre-built integrations, as seen in Introduction - Model Context Protocol.

Importance: Why MCP Matters¶

MCP is crucial for breaking down data silos, a significant barrier in AI development. By providing a standardized way to connect AI with data, it enhances scalability and efficiency, reducing the need for custom integrations. This is particularly valuable in enterprise settings, where AI needs to interact with content repositories, business tools, and development environments. Early adopters, including development tools like Zed and Replit, are integrating MCP to improve context-aware coding, as noted in Anthropic's announcement (Introducing the Model Context Protocol | Anthropic).

Its importance also lies in security and flexibility. MCP follows best practices for securing data within infrastructure, ensuring controlled access, and allows switching between LLM providers without reconfiguring integrations. However, its adoption is still evolving, with some debate around support for remote hosts, currently in active development, as mentioned in For Server Developers - Model Context Protocol.

List of Open-Source MCP Servers¶

Several open-source MCP servers are available, catering to various use cases. Below is a table summarizing key servers, based on community repositories and official listings:

Server Name	Description	Repository/Link
Python SDK	Official Python implementation for MCP servers/clients	Model Context Protocol Python SDK
ChatSum	Summarizes chat messages using LLMs	GitHub - modelcontextprotocol/servers
Chroma	Vector database for semantic document search	GitHub - modelcontextprotocol/servers
ClaudePost	Enables email management for Gmail	GitHub - modelcontextprotocol/servers
Cloudinary	Uploads media to Cloudinary and retrieves details	GitHub - modelcontextprotocol/servers
AWS S3	Fetches objects from AWS S3, e.g., PDF documents	GitHub - modelcontextprotocol/servers
Airtable	Read/write access to Airtable databases	GitHub - modelcontextprotocol/servers

This list is not exhaustive, and for a broader collection, refer to Awesome MCP Servers, which includes community-contributed servers like MCP-Zotero for Zotero Cloud integration and MCP-Geo for geocoding services.

Integration with Cursor and Claude¶

Using MCP with Claude¶

MCP integration with Claude is primarily through the Claude Desktop application. To use it:

Ensure you have the latest Claude Desktop installed, available at Claude Desktop Downloads.
Enable developer mode by opening Settings from the menu and navigating to the Developer option.
Edit the claude_desktop_config.json file (located at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS) to add MCP servers. For example, to add a filesystem server:

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

Restart Claude Desktop to apply changes. The MCP tools will appear as icons (e.g., a hammer) in the input box, allowing interaction with server capabilities.

This process is detailed in For Claude Desktop Users - Model Context Protocol, which also notes that MCP currently supports only desktop hosts, with remote hosts in development.

Using MCP with Cursor¶

Cursor, an AI-powered code editor by Anysphere, also supports MCP, enabling custom tool integration. To use MCP:

Open Cursor and navigate to "Features" > "MCP" in the settings.
Click "+ Add New MCP Server" to configure a server, selecting the transport (e.g., stdio) and providing the command or URL.
For example, to add a weather server, you might configure it with a command like npx /path/to/weather-server, as shown in Cursor – Model Context Protocol.

MCP tools in Cursor are available in the Composer Agent, and users can prompt tool usage intentionally. This integration is still emerging, with community discussions on Cursor as an MCP client - Community Forum highlighting its potential for automating software development tasks.

Guide to Building Your Own MCP Server¶

Building your own MCP server involves several steps, leveraging official SDKs and documentation. Here's a detailed guide:

Understand the Protocol: Review the MCP specification at Specification – Model Context Protocol Specification, which covers server features like prompts, resources, and tools.
Choose a Language and SDK: Use official SDKs, such as:
Python: Model Context Protocol Python SDK
Java: Model Context Protocol Java SDK
Kotlin: Model Context Protocol Kotlin SDK
Set Up the Project: Initialize your project with the chosen SDK. For Python, install via pip install modelcontextprotocol, and for Node.js, use npm install modelcontextprotocol/sdk.
Define Server Capabilities: Implement server functions, such as:
Resources: Expose data, e.g., fetching files.
Tools: Define executable actions, e.g., sending emails.
Prompts: Create templates for LLM interactions.
Test Locally: Connect your server to a client like Claude Desktop. Configure the client as shown in For Server Developers - Model Context Protocol, which includes a tutorial for building a weather server.
Deploy and Share: Once tested, deploy your server locally or remotely (note: remote hosts are in development). Consider contributing to the community via GitHub - modelcontextprotocol/servers.

This process requires technical expertise, but the documentation provides examples, such as building a simple word counter tool, as seen in Getting MCP Server Working with Claude Desktop in WSL | Scott Spence.

Conclusion¶

MCP represents a significant step forward in AI integration, offering a standardized approach to connect LLMs with data and tools. Its open-source nature, supported by a growing ecosystem of servers and community contributions, makes it a promising tool for developers. While integration with Cursor and Claude is feasible, its evolving nature suggests ongoing developments, particularly for remote host support. For those looking to extend MCP, building custom servers is accessible with official resources, ensuring a robust foundation for future AI applications.

Key Points¶

MCP, or Model Context Protocol, is likely an open standard for connecting AI to data, developed by Anthropic, with research suggesting it enhances AI integration.
It seems to have originated around November 2024 to address data connectivity challenges for LLMs.
The evidence leans toward MCP enabling AI to access and interact with external data and tools securely.
It appears important for breaking data silos and improving AI scalability, though its adoption is still evolving.
There are open-source servers like Python SDK and Chroma, with ongoing community contributions.
Using MCP with Cursor and Claude involves configuring servers in their respective apps, with details varying by platform.
Building your own MCP server seems feasible with official documentation, but may require technical expertise.