mcp: Model Context Protocol Server

Overview: What is MCP Server?

The mcp project provides a Python-based implementation of an Elasticsearch MCP (Model Context Protocol) server, enabling seamless integration between AI applications and data sources through a standardized protocol. This MCP server acts as the bridge for various AI tools like Claude Desktop, Continue, Cursor, etc., to access specific datasets or external tools via a unified framework.

🔧 Core Features & MCP Capabilities

The core feature of this repository lies in its ability to transform complex interaction needs between AI applications and data sources into simple protocol-based interactions. The server leverages Elasticsearch for efficient data indexing and retrieval, ensuring that AI clients can quickly access the necessary information they need without requiring direct interaction with the underlying database management system.

MCP Protocol Implementation Details

The implementation details of the MCP protocol in this project follow a strict structure:

1. Authentication: Each client must authenticate before accessing the server to ensure secure communication.
2. Command Execution: The server parses and executes commands sent by AI clients, directing them to specific data sources or tools as defined by the context.
3. Data Retrieval & Manipulation: Upon receiving a command, the server retrieves relevant data from Elasticsearch and sends it back to the client in an appropriate format.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the mcp server is designed with modularity and scalability in mind:

• The protocol layer ensures compatibility across various AI clients while abstracting away the complexity of direct database operations.
• Elasticsearch serves as the backbone for data storage, indexing, and retrieval.

Mermaid Diagram: MCP Protocol 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

Mermaid Diagram: Data Architecture

graph TD
    A[MCP Server] --> B[Elasticsearch]
    B --> C[Data Source 1]
    B --> D[Data Source 2]
    A --> E[MCP Client Interface]
    C --> F[Prompt Database]
    D --> G[Tool Execution Gateway]
    style A fill:#f3e5f5
    style B fill:#e8f5e8
    style C fill:#f0f8ff
    style D fill:#f0f8ff

🚀 Getting Started with Installation

To get started, ensure you have the necessary prerequisites installed:

1. Install Claude Desktop: This is a requirement for interacting directly with the server.
2. Elasticsearch Instance: Run an Elasticsearch instance to power the MCP protocol data management.

Once the dependencies are in place, follow these steps:

• Configure the .env file appropriately based on your setup.
• Execute the commands make add-claude-config and make run to start the server.

Example Code Sample for Configuration

Here’s an example of how you might configure the MCP server:

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

💡 Key Use Cases in AI Workflows

1. Content Creation and Revision: Authors can use the server to quickly gather relevant data from various sources, allowing for efficient content creation and revision processes.
2. Data Analysis and Reporting: Analysts can leverage the server to access structured and unstructured data, perform analyses, and generate reports.

Real-World AI Workflow 1: Content Creation

An author uses Claude Desktop (an MCP client) to draft an essay on climate change. The author interacts with several sources through the MCP protocol to gather relevant data points:

# Example of calling the MCP server from Claude Desktop
server = mcp_client('your-server-name', api_key='your-api-key')
response = server.get_data('source-id:climate-change-research')

content = response.data['text']

Real-World AI Workflow 2: Data Analysis

A data scientist uses Continue (another MCP client) to analyze survey responses stored in Elasticsearch:

# Example of calling the MCP server from Continue
server = mcp_client('your-server-name', api_key='your-api-key')
responses = server.get_data('source-id:survey_responses')

analysis_result = perform_analysis(responses)
print(analysis_result)

🔌 Integration with MCP Clients

The server supports a variety of clients, ensuring broad compatibility and flexibility in AI application integration. Some notable compatibility include:

• Claude Desktop: Full Support for interaction through the protocol.
• Continue: Full Support for data access.
• Cursor: Limited support for tools only.

📊 Performance & Compatibility Matrix

The following table outlines the current state of MCP client compatibility with the server:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🛠️ Advanced Configuration & Security

For advanced usage, the server supports:

• API Key Management: Secure access control for clients.
• Customization of Resource Access Policies: Fine-grained control over what data and tools can be accessed by different users or client instances.

Example Code Sample for API Key Management

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    },
    "customPolicies": [
      {
        "api_key": "admin-api-key",
        "policies": {
          "*": true
        }
      },
      {
        "api_key": "user-api-key",
        "policies": {
          "source-id:weather_data": true,
          "other-source": false
        }
      }
    ]
  }
}

❓ Frequently Asked Questions (FAQ)

1. Q: How do I integrate my custom tool with the MCP server?

   • A: You can write a custom client using the provided MCP protocol or extend an existing one.

2. Q: Can all AI clients access the same data sources?

   • A: Only clients that are granted permission through API key policies can access specific data sources.

3. Q: How do I troubleshoot connection issues with my MCP server?

   • A: Check your .env file for correct configurations and ensure your Elasticsearch instance is running properly.

4. Q: Does the server support multi-tenancy out of the box?

   • A: Yes, you can configure different API keys to enable multiple tenants using distinct access policies.

5. Q: Can I enhance data security further than what’s provided by default?

   • A: You can implement additional encryption and authentication methods as needed, leveraging standard security practices.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! Follow these steps to contribute:

1. Fork the repository.
2. Clone your forked repository.
3. Create a new branch for your feature: git checkout -b feature-name.
4. Commit your changes and push them back to GitHub.
5. Open a pull request.

🌐 MCP Ecosystem & Resources

• Official Documentation: MCP Protocol Official Docs
• Community Forum: Join our community on Discord for real-time support: [Discord Server Link]
• Bug Reporting: Report any bugs encountered on the GitHub issue tracker: [GitHub Issue Tracker Link]

By leveraging the mcp server, developers can significantly enhance their AI applications by providing a robust and scalable framework for integrating with diverse data sources and tools.