Empowering LLMs with Aiven: A Deep Dive into the Model Context Protocol Server

This document details the Aiven Model Context Protocol (MCP) server, a crucial component for bridging the gap between Large Language Models (LLMs) and the rich ecosystem of Aiven services. By leveraging MCP, developers can create intelligent, context-aware applications that seamlessly interact with Aiven for PostgreSQL, Kafka, ClickHouse, Valkey, OpenSearch, and their associated native connectors. This enables the development of comprehensive, full-stack AI solutions tailored to diverse use cases.

Core Capabilities

The Aiven MCP server provides a suite of powerful tools designed to expose Aiven's capabilities to LLMs:

• list_projects: Retrieves a comprehensive list of all projects associated with your Aiven account. This allows LLMs to understand the organizational structure of your Aiven resources.
• list_services: Enumerates all services within a specified Aiven project. This provides LLMs with a detailed inventory of available resources for a given project.
• get_service_details: Fetches detailed configuration and status information for a specific Aiven service within a project. This enables LLMs to understand the capabilities and current state of individual services.

Seamless Integration with LLM Platforms

The Aiven MCP server is designed for easy integration with popular LLM development environments, including Claude Desktop and Cursor. The following sections provide detailed configuration instructions for each platform.

Configuring Claude Desktop

1. Locate the Configuration File: The Claude Desktop configuration file is located in the following directories:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%/Claude/claude_desktop_config.json

2. Add the MCP Server Configuration: Insert the following JSON snippet into the mcpServers section of the configuration file:

   {
     "mcpServers": {
       "mcp-aiven": {
         "command": "uv",
         "args": [
           "--directory",
           "$REPOSITORY_DIRECTORY",
           "run",
           "--with-editable",
           "$REPOSITORY_DIRECTORY",
           "--python",
           "3.13",
           "mcp-aiven"
         ],
         "env": {
           "AIVEN_BASE_URL": "https://api.aiven.io",
           "AIVEN_TOKEN": "$AIVEN_TOKEN"
         }
       }
     }
   }

3. Environment Variable Configuration: Update the following environment variables to reflect your specific environment:

   • $REPOSITORY_DIRECTORY: The absolute path to the directory containing the Aiven MCP server repository.
   • $AIVEN_TOKEN: Your Aiven authentication token. You can generate a token from the Aiven console as described here.

4. uv Executable Path: Locate the command entry for uv and replace it with the absolute path to the uv executable. This ensures that Claude Desktop uses the correct version of uv. You can determine the path using the which uv command on macOS or by examining your system's environment variables.

5. Restart Claude Desktop: Restart Claude Desktop to apply the configuration changes.

Configuring Cursor

1. Access Cursor Settings: Navigate to Cursor -> Settings -> Cursor Settings.

2. Select MCP Servers: Choose the "MCP Servers" option.

3. Add a New Server: Create a new server with the following configuration:

   • Name: mcp-aiven
   • Type: command
   • Command: uv --directory $REPOSITORY_DIRECTORY run --with-editable $REPOSITORY_DIRECTORY --python 3.13 mcp-aiven

   Replace $REPOSITORY_DIRECTORY with the actual path to the Aiven MCP server repository.

4. Environment Variables (Optional): You may need to add the AIVEN_BASE_URL, AIVEN_PROJECT_NAME, and AIVEN_TOKEN environment variables within the Cursor settings, depending on your environment configuration.

Development Environment Setup

To contribute to the Aiven MCP server or to customize it for your specific needs, follow these steps to set up your development environment:

1. Environment Variables: Create a .env file in the root directory of the repository and add the following variables:

   AIVEN_BASE_URL=https://api.aiven.io
   AIVEN_TOKEN=$AIVEN_TOKEN
   

   Replace $AIVEN_TOKEN with your actual Aiven API token.

2. Install Dependencies: Run uv sync to install the required dependencies. If you don't have uv installed, follow the instructions here. After installation, activate the virtual environment using source .venv/bin/activate.

3. Run the MCP Server: For testing purposes, you can start the MCP server using the command mcp dev mcp_aiven/mcp_server.py.

Essential Environment Variables

The Aiven MCP server relies on the following environment variables for proper configuration:

Required Variables

• AIVEN_BASE_URL: The base URL of the Aiven API endpoint (e.g., https://api.aiven.io).
• AIVEN_TOKEN: Your Aiven authentication token, granting access to your Aiven resources.

Security and Responsibility: A Critical Perspective

When integrating LLMs with external systems via MCPs, it's paramount to understand the shared responsibility model and the associated security implications.

Self-Managed MCPs: User Responsibility

• Operational Control: Aiven does not host or manage MCPs. They execute within the user's environment. This means users are entirely responsible for the MCP's operation, security, compliance, and maintenance. This aligns with Aiven's shared responsibility model (refer to https://aiven.io/responsibility-matrix for details).
• Deployment and Updates: Developers are responsible for deploying, updating, and maintaining the MCP infrastructure.

AI Agent Security: A Focus on Permissions

• API Token Permissions: The capabilities of AI Agents are directly tied to the permissions granted to the API token used for authentication. Careful management of these permissions is crucial.
• Credential Handling: A High-Risk Area: AI Agents often require access credentials (e.g., database connection strings, streaming service tokens) to interact with Aiven services. Exercise extreme caution when providing these credentials to AI Agents. Improper handling can lead to significant security breaches.
• Risk Assessment: A Mandatory Step: Before granting AI Agents access to sensitive resources, conduct a thorough risk assessment in accordance with your organization's security policies.

API Token Best Practices: Minimizing Risk

• Principle of Least Privilege: The Golden Rule: Always adhere to the principle of least privilege. API tokens should be scoped to the minimum permissions required for their intended function. Avoid granting broad, unrestricted access.
• Token Management: A Continuous Process: Implement robust token management practices, including regular rotation and secure storage. Treat API tokens as highly sensitive secrets.

Key Takeaways: A Summary of Responsibilities

• User Control: Users retain full control and responsibility for MCP execution and security.
• Token-Based Permissions: AI Agent permissions are directly determined by the permissions of the API token used for authentication.
• Credential Security: Exercise extreme caution when providing credentials to AI Agents.
• Least Privilege: Strictly adhere to the principle of least privilege when managing API tokens.

By carefully considering these security implications and adhering to best practices, developers can leverage the power of LLMs and Aiven services while maintaining a robust security posture.