MCP Server for TrueRAG Policies API MCP Server: Enhancing AI Application Integration

Overview: What is the MCP Server for TrueRAG Policies API?

The MCP (Model Context Protocol) server for TrueRAG Policies API is a robust implementation designed to facilitate seamless integration between various AI applications and specific data sources or tools. This server adheres to the Model Context Protocol, which acts as a universal adapter ensuring interoperability among different AI platforms through a standardized protocol.

🔧 Core Features & MCP Capabilities

The core features of this MCP server include real-time policy enforcement via a GraphQL API. By leveraging the Model Context Protocol SDK for Python, it enables secure and efficient data interactions between the server and the target TrueRAG system. The implementation utilizes the GQL library to interact with the GraphQL API, ensuring flexible and dynamic queries that can be tailored to specific use cases.

⚙️ MCP Architecture & Protocol Implementation

The architecture of this MCP server is meticulously designed to ensure compatibility across multiple AI clients. It employs a modular approach where the MCP protocol flow is decoupled from data processing logic, enabling easy upgrades and maintenance. Here’s an overview of its key components:

MCP Protocol Flow Diagram

graph TD
    A[AI Application] -->|MCP Client| B[MCP Server]
    B --> C[Policies API]
    C --> D[TrueRAG System]
    style A fill:#e1f5fe
    style B fill:#b4e2df
    style C fill:#f3e5f5
    style D fill:#d0e8e9

Data Architecture Diagram

graph TD;
    A[API Gateway] --> B[MCP Server]
    A --> C[Policies API Endpoints]
    B -->|Queries/Data Fetching| D[Data Sourcing Layer]
    B --> E[Traffic Management]
    C --> F[Response Processing]
    C --> G[Hypertext Application Language (HATEOAS)]
    D --> H[Persistent Cache/MongoDB]
    F --> I[Logging & Monitoring System]
    G --> J[User Feedback Module]
    style A fill:#e1f5fe
    style B fill:#b4e2df
    style C fill:#d0e8e9

🚀 Getting Started with Installation

To begin using the MCP server for TrueRAG Policies API, follow these steps:

Clone the Repository

git clone https://github.com/Ad-Veritas/mcp-server-trueRAG.git
cd mcp-server-trueRAG

Ensure Dependents Are Installed

Make sure that uv is installed by running:

uv --version

If not, you can install it as follows:

macOS and Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Define Environment Variables

Create a .env file in the root directory of the repository and add:

GRAPHQL_API_KEY = "{your_api_key}"
GRAPHQL_ENDPOINT = "{your_graphql_endpoint}"

Replace {your_api_key} and {your_graphql_endpoint} with your actual keys.

💡 Key Use Cases in AI Workflows

This MCP server is instrumental in various AI workflows, offering a centralized point of control for policy enforcement. Here are two realistic use cases:

Use Case 1: Real-Time Policy Application

Imagine an e-commerce platform where real-time policy checks need to be enforced on product listings. The MCP server can intercept GraphQL queries from the AI application and apply relevant policies before returning results. This ensures compliance with internal regulations and best practices.

Use Case 2: Customizable Data Access Controls

In a research lab setting, researchers might require fine-grained access controls over experimental data. By integrating this MCP server, they can dynamically adjust permissions based on user roles or project-specific scopes, ensuring secure data handling while maintaining flexibility in exploration.

🔌 Integration with MCP Clients

The following table outlines the compatibility of this server with various MCP clients:

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

📊 Performance & Compatibility Matrix

The server is optimized for performance, ensuring quick response times and efficient data retrieval. It supports various versions of the MCP protocol, providing backward compatibility while maintaining forward progress.

🛠️ Advanced Configuration & Security

Optional Custom Configurations

You can customize the configuration file to tailor it more closely to your needs:

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

Security Best Practices

Implement robust security measures such as API key management, rate limiting, and authentication to protect the data and services provided by this server.

❓ Frequently Asked Questions (FAQ)

1. Q: How do I integrate an AI application with this MCP server?

   • A: You can integrate it by configuring your AI client to use the Model Context Protocol through a GraphQL API that interacts with our MCP server.

2. Q: What are the supported MCP clients for TrueRAG Policies API?

   • A: Currently, Claude Desktop and Continue are fully supported, while Cursor only supports tool integration without prompts capability.

3. Q: Can this server be deployed in different environments (e.g., cloud, on-premise)?

   • A: Yes, it is designed to be flexible and can be deployed in various environments with appropriate configurations.

4. Q: Is there a method for customizing the MCP protocol messages?

   • A: Customization can be achieved by adjusting the mcpServers configuration within your environment variables or custom config files.

5. Q: How does this server ensure data privacy and security?

   • A: The server employs secure API key management, authentication mechanisms, and robust data handling practices to maintain data privacy and security.

👨‍💻 Development & Contribution Guidelines

Contributions are highly welcome! To contribute, follow these steps:

1. Fork the repository on GitHub.
2. Clone your fork locally:

   git clone https://github.com/your-username/mcp-server-trueRAG.git
   

3. Make changes and tests.
4. Commit your changes:

   git commit -m "Add support for new protocol version"
   

5. Push the changes to GitHub:

   git push origin main
   

6. Submit a pull request.

🌐 MCP Ecosystem & Resources

For more resources and insights, visit:

• Model Context Protocol Documentation
• GitHub Repository for TrueRAG Policies API

By leveraging this MCP server for TrueRAG Policies API, developers can streamline the integration of AI applications with diverse data sources and tools, ensuring both efficiency and compliance.