MCP Collection: A Multi-Serving Platform for Model Context Protocol

Overview: What is MCP Collection?

MCP-Collection (Model Context Protocol collection) is an innovative Docker image that bundles multiple Model Context Protocol (MCP) servers, allowing developers to easily switch between different server implementations at runtime. This flexibility enables a seamless integration of various AI applications with data sources and tools through a standardized protocol. The bundle comes packaged with the supergateway, which facilitates communication protocols such as HTTP/SSE or WebSocket, making it remote-ready out of the box.

🔧 Core Features & MCP Capabilities

MCP-Collection simplifies deployment by providing several key features:

1. One Image – Many Servers: Users can choose the desired MCP server by passing a single runtime argument.
2. Remote-Ready: The bundled supergateway acts as an adapter, bridging MCP servers' stdio interface to more familiar protocols like HTTP/SSE or WebSocket.
3. Kubernetes-First: Designed with Kubernetes deployment in mind, this image includes baked-in health endpoints and runs non-root, contributing to a secure container environment. Additionally, it features the smallest possible runtime layer of less than 80MB.
4. Deterministic Builds: Python dependencies are locked within uv.lock, preventing run-time installation or modification.

These features make MCP-Collection an ideal choice for developers working on complex AI workflows that require integration with multiple tools and data sources through a single consistent interface.

⚙️ MCP Architecture & Protocol Implementation

MCP-Collection leverages the Model Context Protocol to enable seamless communication between various AI applications. The protocol's architecture supports a rich set of commands, making it highly adaptable to different types of interactions such as prompting, fetching, and managing data sources. The core structure of each MCP server within the collection is implemented using both Python and Node.js environments, ensuring broad compatibility across diverse use cases.

Mermaid Diagram: Model Context Protocol (MCP) Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram illustrates that AI applications use the MCP client to communicate with the Model Context Protocol, which then routes commands and data to the appropriate server (in this case, MCPServer-Collection), ensuring secure and efficient interactions between the application and underlying data sources.

🚀 Getting Started with Installation

To get started using MCP-Collection, follow these simple steps:

1. Pull the Multi-Arch Image:

   docker pull ghcr.io/erhardtconsulting/mcp-collection:1.0.0
   

2. List Available MCP Servers (shows help):

   docker run --rm ghcr.io/erhardtconsulting/mcp-collection
   

3. Run the Git Server with Extra Flags for the Underlying CLI:

   docker run --rm -p 8080:8080 \
     ghcr.io/erhardtconsulting/mcp-collection mcp-server-fetch --test
   

💡 Key Use Cases in AI Workflows

MCP-Collection can significantly enhance the development and deployment of AI workflows by simplifying interactions with various tools and data sources. Two notable use cases include:

1. Prompt Generation: An AI application can dynamically generate prompts for text analysis, allowing users to define specific contexts or queries.
2. Data Fetching: Another scenario involves fetching real-time data analytics directly from databases or APIs, ensuring that the application has up-to-date information.

Mermaid Diagram: Data Architecture

graph TD
    A[AI Application] -->|MCP Client| B[MCP Collection]
    B --> C[MCP Server Fetch]
    C --> D[SQL Database]
    D --> E[Real-Time Database]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8
    style E fill:#d4ffce

In this diagram, the AI application communicates with MCP Collection, which then routes commands to specific servers. For instance, data fetching capabilities can draw from SQL or real-time databases, ensuring that the AI workflows are well-equipped with necessary information.

🔌 Integration with MCP Clients

MCP-Collection ensures compatibility with a wide array of AI applications and clients through its standardized protocol. Key clients include:

• Claude Desktop: Full Support for data fetching and prompt generation.
• Continue: Complete integration, enabling advanced analysis tools.
• Cursor: Limited to tool-based integrations.

Configuration Sample

Here’s an example of how you might configure the MCP servers within MCP-Collection:

{
  "mcpServers": {
    "git-fetch": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-fetch"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This JSON snippet provides a basic configuration for the git-fetch server, integrating it with MCP-Collection and specifying an API key for authentication.

📊 Performance & Compatibility Matrix

MCP-Collection is designed to be highly compatible across a wide range of AI applications. Here’s a compatibility matrix detailing the support status for each client:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

🛠️ Advanced Configuration & Security

Container Security Measures

• Non-Root User: The container runs as a non-root user (1000:1000), reducing potential security risks.
• NoShell After EXEC: No shell is kept post-execution, ensuring the attack surface remains minimized.

Using Reverse Proxies or API-Gateways

For enhanced security and external accessibility, consider deploying MCP-Collection behind a reverse proxy or API gateway. These solutions can enforce TLS and authentication mechanisms to secure data transmission.

❓ Frequently Asked Questions (FAQ)

1. Q: How do I configure different MCP servers within the image?

   • A: You can specify which server to run using a single argument at runtime. The configuration is detailed in the entrypoint.sh file, allowing you to define and modify server parameters as needed.

2. Q: Can I add new MCP clients or update existing ones?

   • A: Yes, contributors can fork the repository, add/update MCP servers, and open a pull request for code review. Ensure that changes are tested thoroughly before submission.

3. Q: What environments does MCP-Collection support?

   • A: MCP-Collection supports both Python and Node.js environments, making it versatile for various AI client needs.

4. Q: How do I handle authentication and authorization with different servers?

   • A: Authentication can be managed through environment variables or configuration files. Each server has its own setup which you need to define in the pyproject.toml/package.json.

5. Q: Can MCP-Collection be deployed on Kubernetes?

   • A: Absolutely! The pre-built image includes baked-in health endpoints and a non-root user, making it well-suited for deployment using Kubernetes.

👨‍💻 Development & Contribution Guidelines

1. Fork the Repository: Begin by forking the MCP-Collection repository onto your own GitHub account.
2. Update MCP Servers: Modify or add new servers to either root/app/package.json (Node) or root/app/pyproject.toml (Python). Also, update the corresponding configuration in entrypoint.sh.
3. Run Local Tests: Use the make test command to run local tests and ensure everything is working as expected.
4. Open a Pull Request: After your changes are complete and tested, open a PR on the original GitHub repository for thorough review.

🌐 MCP Ecosystem & Resources

MCP-Collection is part of a broader ecosystem aimed at standardizing interactions between AI applications and data sources. By contributing to this project, you can actively shape how future AI workflows integrate with diverse tools and platforms. For more information on the MCP Protocol and its applications, visit the official Model Context Protocol documentation.

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By leveraging MCP-Collection, developers can streamline their AI application development process by ensuring consistent and reliable communication between various servers and clients. This document aims to provide a comprehensive guide for both new users and seasoned contributors to fully utilize the capabilities of MCP-Collection in building robust and scalable AI ecosystems.