by lishenxydlgzs
AWS Athena connector lets you run SQL on AWS data lakes via Athena in AWS, empowering rapid, large-scale business intell
Execute SQL queries against AWS Athena databases to analyze large-scale data in AWS data lakes. Requires AWS credentials and S3 bucket for query results.
AWS Athena is a community-built MCP server published by lishenxydlgzs that provides AI assistants with tools and capabilities via the Model Context Protocol. AWS Athena connector lets you run SQL on AWS data lakes via Athena in AWS, empowering rapid, large-scale business intell It is categorized under databases, cloud infrastructure.
You can install AWS Athena in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
AWS Athena is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Enable Claude to query your database directly using natural language
Example
Ask 'Show me top 10 customers by revenue this month' and get SQL results instantly
Eliminate manual SQL writing for ad-hoc queries, get insights 10x faster
Generate complex reports and analytics without leaving conversation
Example
Analyze sales trends, cohort retention, user behavior patterns conversationally
Democratize data access—non-technical team members can query databases
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Share your MCP server with the developer community
AWS Athena has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, AWS Athena benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired AWS Athena into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, AWS Athena benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
AWS Athena is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired AWS Athena into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
AWS Athena is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
AWS Athena has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
AWS Athena is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Useful MCP listing: AWS Athena is the kind of server we cite when onboarding engineers to host + tool permissions.
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A Model Context Protocol (MCP) server for running AWS Athena queries. This server enables AI assistants to execute SQL queries against your AWS Athena databases and retrieve results.
<a href="https://glama.ai/mcp/servers/0i7dhkex6t"> <img width="380" height="200" src="https://glama.ai/mcp/servers/0i7dhkex6t/badge" alt="aws-athena-mcp MCP server" /> </a>Configure AWS credentials using one of the following methods:
AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)Add the server to your MCP configuration:
{
"mcpServers": {
"athena": {
"command": "npx",
"args": ["-y", "@lishenxydlgzs/aws-athena-mcp"],
"env": {
// Required
"OUTPUT_S3_PATH": "s3://your-bucket/athena-results/",
// Optional AWS configuration
"AWS_REGION": "us-east-1", // Default: AWS CLI default region
"AWS_PROFILE": "default", // Default: 'default' profile
"AWS_ACCESS_KEY_ID": "", // Optional: AWS access key
"AWS_SECRET_ACCESS_KEY": "", // Optional: AWS secret key
"AWS_SESSION_TOKEN": "", // Optional: AWS session token
// Optional server configuration
"ATHENA_WORKGROUP": "default_workgroup", // Optional: specify the Athena WorkGroup
"QUERY_TIMEOUT_MS": "300000", // Default: 5 minutes (300000ms)
"MAX_RETRIES": "100", // Default: 100 attempts
"RETRY_DELAY_MS": "500" // Default: 500ms between retries
}
}
}
}
run_query: Execute a SQL query using AWS Athena
get_status: Check the status of a query execution
get_result: Retrieve results for a completed query
list_saved_queries: List all saved (named) queries in Athena.
Returns:
id, name, and optional descriptionATHENA_WORKGROUP and AWS_REGIONrun_saved_query: Run a previously saved query by its ID.
Parameters:
namedQueryId: ID of the saved querydatabaseOverride: Optional override of the saved query's default databasemaxRows: Maximum number of rows to return (default: 1000)timeoutMs: Timeout in milliseconds (default: 60000)Returns:
run_query: full results or execution IDMessage to AI Assistant:
List all databases in Athena
MCP parameter:
{
"database": "default",
"query": "SHOW DATABASES"
}
Message to AI Assistant:
Show me all tables in the default database
MCP parameter:
{
"database": "default",
"query": "SHOW TABLES"
}
Message to AI Assistant:
What's the schema of the asin_sitebestimg table?
MCP parameter:
{
"database": "default",
"query": "DESCRIBE default.asin_sitebestimg"
}
Message to AI Assistant:
Show some rows from my_database.mytable
MCP parameter:
{
"database": "my_database",
"query": "SELECT * FROM my_table LIMIT 10",
"maxRows": 10
}
Message to AI Assistant:
Find the average price by category for in-stock products
MCP parameter:
{
"database": "my_database",
"query": "SELECT category, COUNT(*) as count, AVG(price) as avg_price FROM products WHERE in_stock = true GROUP BY category ORDER BY count DESC",
"maxRows": 100
}
{
"queryExecutionId": "12345-67890-abcdef"
}
{
"queryExecutionId": "12345-67890-abcdef",
"maxRows": 10
}
{
"name": "list_saved_queries",
"arguments": {}
}
{
"name": "run_saved_query",
"arguments": {
"namedQueryId": "abcd-1234-efgh-5678",
"maxRows": 100
}
}
ATHENA_WORKGROUP and AWS_REGIONMIT
Run data quality queries to catch anomalies and inconsistencies
Example
Find duplicate records, missing values, orphaned foreign keys automatically
Maintain data integrity with less manual SQL work
Prerequisites
Time Estimate
15-30 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
Compatibility
✓ Use when
Use for ad-hoc data queries, exploratory analysis, report generation, schema exploration, and democratizing data access. Best for read-heavy analytics workloads.
✗ Avoid when
Avoid for production write operations, mission-critical transactions, real-time OLTP workloads, or when database contains sensitive PII without proper access controls. Use read replicas, not primary.