AWS Athena▌
by lishenxydlgzs
AWS Athena connector lets you run SQL on AWS data lakes via Athena in AWS, empowering rapid, large-scale business intell
Integrates with AWS SDK to execute SQL queries against Athena databases, enabling large-scale data analysis and business intelligence for AWS data lakes.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Data analysts working with AWS data lakes
- / Business intelligence teams using AWS infrastructure
- / Backend developers building data-driven applications
capabilities
- / Execute SQL queries on AWS Athena databases
- / Retrieve query results from data lake sources
- / Configure custom workgroups and query timeouts
- / Handle large-scale data analysis queries
what it does
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.
about
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.
how to install
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.
license
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.
readme
@lishenxydlgzs/aws-athena-mcp
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>Usage
-
Configure AWS credentials using one of the following methods:
- AWS CLI configuration
- Environment variables (
AWS_ACCESS_KEY_ID,AWS_SECRET_ACCESS_KEY) - IAM role (if running on AWS)
-
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
}
}
}
}
- The server provides the following tools:
-
run_query: Execute a SQL query using AWS Athena- Parameters:
- database: The Athena database to query
- query: SQL query to execute
- maxRows: Maximum number of rows to return (default: 1000, max: 10000)
- Returns:
- If query completes within timeout: Full query results
- If timeout reached: Only the queryExecutionId for later retrieval
- Parameters:
-
get_status: Check the status of a query execution- Parameters:
- queryExecutionId: The ID returned from run_query
- Returns:
- state: Query state (QUEUED, RUNNING, SUCCEEDED, FAILED, or CANCELLED)
- stateChangeReason: Reason for state change (if any)
- submissionDateTime: When the query was submitted
- completionDateTime: When the query completed (if finished)
- statistics: Query execution statistics (if available)
- Parameters:
-
get_result: Retrieve results for a completed query- Parameters:
- queryExecutionId: The ID returned from run_query
- maxRows: Maximum number of rows to return (default: 1000, max: 10000)
- Returns:
- Full query results if the query has completed successfully
- Error if query failed or is still running
- Parameters:
-
list_saved_queries: List all saved (named) queries in Athena. -
Returns:
- An array of saved queries with
id,name, and optionaldescription - Queries are returned from the configured
ATHENA_WORKGROUPandAWS_REGION
- An array of saved queries with
-
run_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:
- Same behavior as
run_query: full results or execution ID
- Same behavior as
Usage Examples
Show All Databases
Message to AI Assistant:
List all databases in Athena
MCP parameter:
{
"database": "default",
"query": "SHOW DATABASES"
}
List Tables in a Database
Message to AI Assistant:
Show me all tables in the default database
MCP parameter:
{
"database": "default",
"query": "SHOW TABLES"
}
Get Table Schema
Message to AI Assistant:
What's the schema of the asin_sitebestimg table?
MCP parameter:
{
"database": "default",
"query": "DESCRIBE default.asin_sitebestimg"
}
Table Rows Preview
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
}
Advanced Query with Filtering and Aggregation
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
}
Checking Query Status
{
"queryExecutionId": "12345-67890-abcdef"
}
Getting Results for a Completed Query
{
"queryExecutionId": "12345-67890-abcdef",
"maxRows": 10
}
Listing Saved Queries
{
"name": "list_saved_queries",
"arguments": {}
}
Running a Saved Query
{
"name": "run_saved_query",
"arguments": {
"namedQueryId": "abcd-1234-efgh-5678",
"maxRows": 100
}
}
Requirements
- Node.js >= 16
- AWS credentials with appropriate Athena and S3 permissions
- S3 bucket for query results
- Named queries (optional) must exist in the specified
ATHENA_WORKGROUPandAWS_REGION
License
MIT
Repository
FAQ
- What is the AWS Athena MCP server?
- AWS Athena is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for AWS Athena?
- This profile displays 32 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.4 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Direct Database Queries from AI
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
Data Analysis & Reporting
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
Schema Exploration
Understand database structure, relationships, and data models
Example
'Explain the user_orders table schema and its relationships'
Onboard engineers faster, explore unfamiliar databases efficiently
Data Validation & Quality Checks
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor with MCP support
- ›Database credentials (read-only recommended for safety)
- ›Network access from Claude client to database
- ›Understanding of database security and access control
Time Estimate
15-30 minutes including configuration and testing
Installation Steps
- 1.Install MCP server: npm install -g @modelcontextprotocol/server-[name]
- 2.Configure database connection in Claude Desktop config (~/.claude/mcp.json)
- 3.Provide connection string: host, port, database, username, password
- 4.Restart Claude Desktop to load MCP server
- 5.Test connection: 'List all tables in database'
- 6.Run simple query: 'Show me 5 rows from users table'
- 7.Verify results and permissions are correct
- 8.Document query patterns for team use
Troubleshooting
- ⚠Connection refused: Check database is running and network accessible
- ⚠Authentication failed: Verify credentials, check user permissions
- ⚠Claude can't see tables: Grant appropriate read permissions to database user
- ⚠Slow queries: Add indexes, limit result set size, use read replicas
- ⚠MCP server not loading: Check config syntax, restart Claude Desktop
Best Practices▌
✓ Do
- +Use read-only database credentials to prevent accidental writes
- +Connect to read replica, not production primary database
- +Set query timeout limits to prevent long-running queries
- +Document database schema and common queries for AI context
- +Monitor query performance and optimize slow queries
- +Use connection pooling for better performance
- +Test with non-production data first
✗ Don't
- −Don't use production write credentials—risk of data corruption
- −Don't query production database during peak traffic hours
- −Don't expose sensitive PII without proper access controls
- −Don't skip query result validation—AI can misinterpret schema
- −Don't allow unlimited result set sizes—set LIMIT clauses
- −Don't share database credentials in plain text config files
💡 Pro Tips
- ★Create database views for common queries to simplify AI access
- ★Add schema comments/descriptions so AI understands column meanings
- ★Use semantic table/column names ('customer_lifetime_value' not 'clv')
- ★Set up query logging to audit what Claude is querying
- ★Create saved query templates for recurring analysis
- ★Combine with data visualization tools for better insights
Technical Details▌
Architecture
MCP server acts as bridge between Claude and database, translating natural language to SQL queries and returning results in structured format.
Protocols
- Model Context Protocol (MCP)
- Database-specific protocols (PostgreSQL, MySQL, MongoDB)
Compatibility
- PostgreSQL
- MySQL
- SQLite
- MongoDB
- Redis
When to Use This▌
✓ 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.
Integration▌
- →Read replica connection for analytics queries
- →Database view layer to abstract complex joins
- →Query result caching for repeated questions
- →Audit logging of all AI-generated queries
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.4★★★★★32 reviews- ★★★★★Tariq Jackson· Dec 16, 2024
AWS Athena has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Dhruvi Jain· Dec 8, 2024
According to our notes, AWS Athena benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Oshnikdeep· Nov 27, 2024
We wired AWS Athena into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Anika Jackson· Nov 15, 2024
According to our notes, AWS Athena benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Tariq Chen· Nov 7, 2024
AWS Athena is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Tariq Patel· Oct 26, 2024
We wired AWS Athena into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Ganesh Mohane· Oct 18, 2024
AWS Athena is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Luis Menon· Oct 6, 2024
AWS Athena has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Charlotte Li· Sep 25, 2024
AWS Athena is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Isabella Kapoor· Sep 17, 2024
Useful MCP listing: AWS Athena is the kind of server we cite when onboarding engineers to host + tool permissions.
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