Frequently Asked Questions

The Model Context Protocol (MCP) is an open protocol that standardizes how AI models connect to external data sources and tools. It provides a consistent interface for AI applications to access, retrieve, and interact with information from various systems, eliminating the need for custom integrations for each data source or tool.

MCP was developed and open-sourced by Anthropic in 2024. While Anthropic created the initial protocol, MCP is designed as an open standard, and its evolution is influenced by contributions from the broader developer community.

MCP addresses the challenge of connecting AI models to external data sources and tools. Before MCP, developers needed to create custom integrations for each data source, leading to fragmented implementations and maintenance challenges. MCP provides a standardized way for AI models to access information and functionality from various systems, simplifying development and improving interoperability.

MCP is designed as an open, model-agnostic protocol that can be used with any AI model, not just Claude. While Anthropic's Claude was the first major AI assistant to support MCP, the protocol is designed to be used with any AI system, including models from other providers and open-source models. The OpenAI Agents SDK, for example, has implemented MCP support.

MCP differs from other AI integration approaches in several key ways:

• Standardization: MCP provides a consistent interface across different data sources and tools, unlike custom API integrations that require specific code for each integration.
• Security: MCP allows data sources to maintain control over their information and doesn't require sharing API keys with AI providers.
• Platform Independence: Unlike plugin systems that are tied to specific platforms, MCP is platform-agnostic and can work with any AI model or application.
• Comprehensive Solution: MCP provides both context provision and tool invocation capabilities in a unified protocol, whereas other approaches might focus on just one aspect.

Yes, MCP is an open protocol that is free to use. The specification, reference implementations, and SDKs are open source and available under permissive licenses that allow for commercial use.

MCP follows a client-server architecture:

• MCP Clients: Applications that use AI models and need to access external data or functionality (like Claude Desktop or custom AI applications).
• MCP Servers: Components that expose data sources, tools, and functionality to MCP clients (like file system servers, database connectors, or API integrations).

Clients connect to servers, discover available tools through the listTools method, and invoke tools using the callTool method with appropriate parameters. Servers process these requests and return results to the clients, which can then incorporate the information into the AI model's context.

MCP currently supports two main transport mechanisms:

• stdio (Standard Input/Output): Used for local communication between clients and servers running on the same machine. The server runs as a subprocess of the client application.
• HTTP over SSE (Server-Sent Events): Used for remote communication between clients and servers running on different machines or in cloud environments. This enables distributed architectures where servers can run separately from clients.

MCP provides official SDKs for several programming languages:

• TypeScript/JavaScript (for Node.js and browser applications)
• Python
• Java (in collaboration with Spring AI)
• C# (.NET)

The community is working on implementations for additional languages. Since MCP is an open protocol, developers can also create implementations for other languages as needed.

An MCP tool is a functional unit exposed by an MCP server that provides specific capabilities to MCP clients. Each tool has:

• Name: A unique identifier for the tool.
• Description: A human-readable explanation of what the tool does.
• Parameters: A schema defining the inputs the tool expects, usually in JSON Schema format.
• Return Value: The type and structure of data the tool returns.
• Handler: The function that implements the tool's functionality.

Examples of tools include "readFile" for accessing file contents, "executeQuery" for running database queries, or "getWeather" for retrieving weather information.

Yes, MCP can connect to remote services and APIs through MCP servers that implement the necessary integrations. For example, an MCP server could connect to a weather API, a database service, or a content management system. The server handles the communication with the remote service and exposes the functionality to MCP clients through a standardized interface.

MCP servers can provide various types of data, including:

• Text content (documents, emails, messages)
• Structured data (database records, JSON objects)
• Binary data (images, audio, video)
• Metadata (file information, user details)
• System information (operating system details, environment variables)

The protocol is flexible and can handle different data formats, allowing AI models to work with diverse information types.

To get started with MCP, follow these steps:

1. Install the appropriate MCP SDK for your programming language.
2. For client development, learn how to connect to existing MCP servers and use their tools.
3. For server development, understand how to define tools and implement their functionality.
4. Explore the official documentation and examples to understand MCP concepts and best practices.

Our Getting Started guide provides detailed instructions and code examples to help you begin working with MCP.

Yes, you can use existing MCP servers instead of building your own. The MCP ecosystem includes reference implementations of servers for common use cases like file system access, Git repositories, databases, and more. These servers can be easily integrated into your applications without having to implement the functionality yourself.

Check the Ecosystem page for a list of available MCP servers that you can use in your projects.

To create a custom MCP server, follow these general steps:

1. Install the MCP server SDK for your preferred programming language.
2. Define the tools your server will provide, including their names, descriptions, parameter schemas, and return types.
3. Implement the handler functions for each tool, which will execute when the tools are called.
4. Configure the server with metadata like name, description, and version.
5. Start the server and handle requests from MCP clients.

The Getting Started guide includes code examples for creating custom MCP servers in different programming languages.

You can use MCP with Claude through various interfaces:

• Claude Desktop: Claude Desktop provides a user-friendly interface for connecting Claude to MCP servers. You can connect to MCP servers through the application's UI and then interact with Claude naturally.
• Claude API: For programmatic access, you can use the Claude API with MCP. You'll need to set up an MCP client, connect to the desired servers, and then pass the available tools to Claude through the API.
• Claude for Work: Enterprise customers using Claude for Work can connect to MCP servers and use them across their organization.

The Getting Started guide includes specific instructions for using MCP with Claude.

No, you don't need to modify the AI model itself to use MCP. The MCP client handles the communication with MCP servers and provides the retrieved information to the AI model through its standard interfaces (like API calls or application integrations).

For hosted models like Claude, the model provider (e.g., Anthropic) has already implemented the necessary integrations to work with MCP. For open-source models, you would typically use an AI framework or library that supports tool usage and provide the MCP tools through that interface.

Common challenges when implementing MCP include:

• Tool Design: Creating tools with clear descriptions and well-structured parameter schemas that AI models can understand and use effectively.
• Error Handling: Implementing robust error handling to provide informative error messages when tools fail.
• Security Considerations: Ensuring that MCP servers only expose the intended functionality and don't allow unauthorized access to sensitive data.
• Performance Optimization: Optimizing data transfer and caching strategies to minimize latency in MCP interactions.
• Cross-Language Compatibility: Ensuring compatibility when working with MCP implementations in different programming languages.

The Getting Started guide includes best practices for addressing these challenges.

MCP was designed with security as a fundamental consideration. Key security features include:

• Server-Controlled Access: MCP servers maintain control over their resources, determining what data is accessible to clients.
• No API Key Sharing: There's no need to share API keys with LLM providers, as the MCP server handles authentication with external services.
• Clear System Boundaries: The client-server architecture establishes clear boundaries between systems, limiting exposure of sensitive information.
• Local Processing: For sensitive operations, MCP can use local stdio servers to ensure data doesn't leave the user's machine.

While MCP provides these security features, the overall security of an MCP implementation depends on how it's configured and deployed. Developers should follow security best practices when implementing MCP servers and clients.

MCP itself doesn't inherently expose your data to third parties. The protocol is designed to keep data within your control:

• Local Processing: When using stdio servers, data is processed locally on your machine and doesn't leave your environment.
• Server Control: MCP servers control what data is accessible and can implement access controls to restrict what information is shared.
• No API Key Sharing: MCP doesn't require sharing API keys with AI providers, keeping authentication credentials within your infrastructure.

However, when using MCP with AI models, the data that's provided to the model may be processed according to the AI provider's privacy policy. It's important to understand the data handling practices of the AI services you're using with MCP.

Yes, you can restrict what data your MCP server exposes. As the developer of an MCP server, you have full control over what tools and data are available to clients. You can implement access controls, permissions, and validation to ensure that only authorized clients can access specific data or functionality.

For example, you could:

• Limit file access to specific directories
• Restrict database queries to read-only operations or specific tables
• Implement user authentication and authorization for sensitive operations
• Validate and sanitize inputs to prevent security vulnerabilities

MCP is designed to keep sensitive information like API keys within your infrastructure, rather than sharing them with AI providers or third parties. When an MCP server needs to authenticate with an external service, it handles that authentication internally using credentials that you provide when setting up the server.

For example, if you're creating an MCP server that connects to a database, you would configure the server with the database credentials, but those credentials wouldn't be exposed to the MCP client or the AI model. The server would handle authentication with the database and only expose the authorized functionality through the MCP interface.

Yes, here are some best practices for securing MCP implementations:

• Principle of Least Privilege: Design MCP servers to expose only the minimum functionality and data access required for the application.
• Input Validation: Implement thorough validation of tool parameters to prevent injection attacks and other security vulnerabilities.
• Access Controls: Implement authentication and authorization mechanisms for MCP servers, especially when using HTTP/SSE transport.
• Secure Credential Storage: Store API keys, passwords, and other sensitive information securely using appropriate encryption and secret management.
• Regular Security Audits: Regularly review your MCP implementations for potential security issues and update them as needed.
• Use HTTPS: When using HTTP/SSE transport, ensure that connections are secured with HTTPS to protect data in transit.
• Logging and Monitoring: Implement comprehensive logging and monitoring for MCP servers to detect and respond to potential security incidents.

The MCP ecosystem includes a variety of servers for different data sources and tools, including:

• Filesystem Server: For accessing local files and directories
• Git Server: For interacting with Git repositories
• Postgres Server: For querying PostgreSQL databases
• Google Drive Server: For accessing files in Google Drive
• Slack Server: For interacting with Slack workspaces
• GitHub Server: For working with GitHub repositories, issues, and pull requests
• Puppeteer Server: For web browsing and automation
• Browser Extension Server: For interacting with web browsers

The community continues to develop additional servers for various data sources and tools. Check the Ecosystem page for an up-to-date list of available MCP servers.

MCP is used by a diverse range of organizations and individuals, including:

• AI Developers: Building AI applications that need to access external data and tools
• Enterprise Organizations: Connecting AI systems to internal data sources and tools
• Tool Providers: Exposing their tools and data to AI models through a standardized interface
• Individual Users: Using Claude Desktop and other MCP-compatible applications to connect AI models to their local files and data

Early adopters of MCP include companies like Block (Square), Apollo, Zed, Replit, Codeium, and Sourcegraph, who have integrated MCP into their platforms to enhance AI capabilities.

There are several ways to contribute to the MCP ecosystem:

• Develop MCP Servers: Create new MCP servers for data sources or tools that aren't yet supported
• Improve SDKs: Contribute to the official MCP SDKs by adding features, fixing bugs, or improving documentation
• Share Examples: Create and share examples of MCP usage to help others learn and understand the protocol
• Join Discussions: Participate in community forums to share ideas, ask questions, and collaborate with other developers
• Report Issues: Help improve MCP by reporting issues and suggesting enhancements

The MCP project is open source and welcomes contributions from the community. Visit the MCP GitHub organization to learn more about contributing.

MCP resources and documentation are available from several sources:

• Official Documentation: modelcontextprotocol.io provides comprehensive guides, references, and tutorials
• GitHub Repositories: github.com/modelcontextprotocol contains the source code for MCP SDKs, servers, and examples
• Anthropic Documentation: docs.anthropic.com includes information on using MCP with Claude
• Community Forums: The MCP Discussions on GitHub provide a place to ask questions and share ideas
• This Website: Our MCP Hub provides informational resources about MCP

The MCP roadmap includes several exciting developments:

• Remote MCP Servers: Support for remote MCP servers with enterprise-grade authentication, enabling broader deployment options
• Enhanced Security Features: Additional security features to protect sensitive data and ensure secure communication between clients and servers
• Broader Language Support: SDKs for additional programming languages to make MCP accessible to more developers
• Integration with AI Platforms: Deeper integration with AI platforms and frameworks to streamline the use of MCP in AI applications
• Community-Driven Standards: Evolution of the MCP specification based on community feedback and real-world usage

As an open protocol, MCP's evolution will be guided by the needs of the community and the broader AI ecosystem. The goal is to establish MCP as a foundational standard for AI integration, similar to how HTTP became the foundation for web communication.

To stay updated on MCP developments, you can:

• Follow the GitHub Repositories: Watch or star the MCP GitHub repositories to receive notifications about updates
• Join the Community Forums: Participate in MCP Discussions on GitHub to connect with other developers
• Subscribe to Anthropic Updates: Follow Anthropic's news for announcements related to MCP
• Check Documentation Regularly: Visit the official MCP documentation for the latest information