AWS Athena MCP Server

What does “aws-athena” MCP Server do?

The aws-athena MCP Server is a Model Context Protocol (MCP) implementation that empowers AI assistants to execute SQL queries directly against AWS Athena databases. By connecting AI-powered workflows to Athena, this server enables developers and AI agents to retrieve and analyze large-scale data stored in Amazon S3 with ease. The server acts as a bridge between conversational AI and enterprise data infrastructure, making it simple to incorporate robust data querying into automated workflows, code generation, and intelligent applications. Typical tasks include executing SQL statements, retrieving query results, and integrating data-driven insights into development processes, thereby streamlining database operations and accelerating data-centric application development.

List of Prompts

No prompt templates are explicitly mentioned in the available documentation or repository files.

List of Resources

No explicit resources are listed in the documentation or repository files.

List of Tools

• run_query:
  Execute a SQL query using AWS Athena.
  • Parameters:
    • database: The Athena database to query
    • query: The SQL query string
    • maxRows: Maximum number of rows to return (default: 1000, max: 10000)
  • Returns:
    • The results of the query if it completes within the specified timeout.

Use Cases of this MCP Server

• Data Analytics for AI Agents
  Allow AI assistants to run analytical SQL queries on large datasets stored in Amazon S3, enabling automated data exploration and reporting.
• Business Intelligence Automation
  Integrate Athena querying into business dashboards or workflow automation tools, providing up-to-date data insights without manual intervention.
• Data-driven Code Generation
  Enable LLMs to generate or refine code based on live database schemas or sample data retrieved via Athena queries.
• ETL and Data Pipeline Integration
  Use the server within data engineering pipelines to validate, transform, or audit data by executing custom SQL queries programmatically.

How to set it up

Windsurf

1. Ensure you have Node.js installed and AWS credentials configured (via CLI, environment variables, or IAM role).
2. Locate the Windsurf configuration file.
3. Add the aws-athena MCP Server using the following JSON snippet:

   {
     "mcpServers": {
       "athena": {
         "command": "npx",
         "args": ["-y", "@lishenxydlgzs/aws-athena-mcp"],
         "env": {
           "OUTPUT_S3_PATH": "s3://your-bucket/athena-results/"
         }
       }
     }
   }
   

4. Save and restart Windsurf.
5. Verify the setup by attempting a sample query.

Claude

1. Ensure Node.js and AWS credentials are set up.
2. Edit the Claude MCP configuration file.
3. Insert the server config:

   {
     "mcpServers": {
       "athena": {
         "command": "npx",
         "args": ["-y", "@lishenxydlgzs/aws-athena-mcp"],
         "env": {
           "OUTPUT_S3_PATH": "s3://your-bucket/athena-results/"
         }
       }
     }
   }
   

4. Save changes and restart Claude.
5. Test AWS Athena connectivity via the Claude interface.

Cursor

1. Install Node.js and configure AWS credentials.
2. Open Cursor’s settings or configuration file.
3. Add the following snippet:

   {
     "mcpServers": {
       "athena": {
         "command": "npx",
         "args": ["-y", "@lishenxydlgzs/aws-athena-mcp"],
         "env": {
           "OUTPUT_S3_PATH": "s3://your-bucket/athena-results/"
         }
       }
     }
   }
   

4. Save and restart Cursor.
5. Confirm the server is available in the tool list.

Cline

1. Verify Node.js installation and AWS credentials.
2. Edit the Cline MCP configuration.
3. Insert:

   {
     "mcpServers": {
       "athena": {
         "command": "npx",
         "args": ["-y", "@lishenxydlgzs/aws-athena-mcp"],
         "env": {
           "OUTPUT_S3_PATH": "s3://your-bucket/athena-results/"
         }
       }
     }
   }
   

4. Save and restart Cline.
5. Test the connection by running a sample Athena query.

Securing API Keys

Use environment variables to securely store sensitive AWS credentials.
Example configuration with secrets:

{
  "mcpServers": {
    "athena": {
      "command": "npx",
      "args": ["-y", "@lishenxydlgzs/aws-athena-mcp"],
      "env": {
        "OUTPUT_S3_PATH": "s3://your-bucket/athena-results/",
        "AWS_ACCESS_KEY_ID": "${AWS_ACCESS_KEY_ID}",
        "AWS_SECRET_ACCESS_KEY": "${AWS_SECRET_ACCESS_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "athena": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “athena” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

This MCP server is focused and production-ready for AWS Athena SQL querying, with clear setup and secure practices. However, it lacks prompt templates and explicit resource primitives, and does not mention sampling or roots support, limiting its score for versatility and advanced MCP features.

MCP Score