AWS Athena Cost MCP Server

An MCP (Model Context Protocol) server for analyzing AWS Athena query costs. This server provides tools to query, analyze, and compare Athena query execution data stored in PostgreSQL to investigate cost increases and generate reports.

Architecture

┌─────────────────────────────────┐
│   Daily Process (Standalone)    │
│  scripts/daily_fetch_queries.py │
│  - Fetches from AWS Athena API  │
│  - Stores in PostgreSQL         │
│  - Runs via cron                │
└──────────────┬──────────────────┘
               │
               ▼
┌─────────────────────────────────┐
│      PostgreSQL Database        │
│         (queries table)         │
└──────────────┬──────────────────┘
               │
               ▼
┌─────────────────────────────────┐
│      MCP Server Tools          │
│  - fetch_athena_queries         │
│  - compare_expensive_queries    │
│  - analyze_cost_increase        │
└─────────────────────────────────┘

Related MCP server: MCP PostgreSQL Operations

Dashboard Preview

Features

• Daily Data Collection: Automated daily process to fetch query execution data from AWS Athena API for all workgroups

• Fetch Athena Queries: Query PostgreSQL database and export to CSV

• Analyze Cost Increases: Compare baseline vs spike periods to identify cost drivers

• Compare Expensive Queries: Extract patterns and features from expensive queries

Installation

Prerequisites:

• Python 3.10 or higher (required for the mcp package)

• AWS credentials configured

1. Clone this repository:

git clone <repository-url>
cd mcp-aws-cost

2. Create a virtual environment and install dependencies:

# Create virtual environment with Python 3.10+
python3.10 -m venv venv
# Or if you have Python 3.13:
# python3.13 -m venv venv

# Activate the virtual environment
source venv/bin/activate  # On macOS/Linux
# or
# venv\Scripts\activate  # On Windows

# Install dependencies
pip install -r requirements.txt

Note: The mcp package requires Python 3.10 or higher. If your default python or python3 is version 3.9 or lower, you must use python3.10 or python3.13 explicitly.

3. Configure AWS credentials:

   • Set up AWS credentials using aws configure

   • Or set environment variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION

   • Or use IAM roles if running on AWS infrastructure

Usage

Running the MCP Server

The server uses stdio transport and can be run directly:

# Make sure virtual environment is activated
source venv/bin/activate
python -m src.server

Or configure it in your MCP client configuration file (e.g., mcp.json):

{
  "mcpServers": {
    "aws-athena-cost": {
      "command": "python",
      "args": ["-m", "src.server"],
      "env": {
        "AWS_DEFAULT_REGION": "us-east-1"
      }
    }
  }
}

Available Tools

1. fetch_athena_queries

Queries Athena query execution data from PostgreSQL database and exports to CSV. Can query a specific workgroup or all workgroups.

Note: This tool queries the PostgreSQL database only. Data is collected by the daily process (scripts/daily_fetch_queries.py).

Parameters:

• workgroup (string, optional): Athena workgroup name (e.g., "ETL"). If not provided, queries all workgroups

• start_date (string, required): Start date in YYYY-MM-DD format

• end_date (string, required): End date in YYYY-MM-DD format

• output_dir (string, optional): Output directory for CSV (default: ./reports)

Example - Specific workgroup:

{
  "workgroup": "ETL",
  "start_date": "2025-12-10",
  "end_date": "2025-12-16"
}

Example - All workgroups:

{
  "start_date": "2025-12-10",
  "end_date": "2025-12-16"
}

Returns:

• file_path: Path to generated CSV file

• total_processed: Total queries processed

• matched_count: Number of queries matching date range (and workgroup if specified)

2. analyze_cost_increase

Analyzes cost increases by comparing baseline vs spike periods. Can query from PostgreSQL database or read from CSV file.

Parameters:

• csv_file (string, optional): Path to CSV file with query data. If not provided, queries PostgreSQL

• baseline_start (string, required): Baseline period start date (YYYY-MM-DD)

• baseline_end (string, required): Baseline period end date (YYYY-MM-DD)

• spike_start (string, required): Spike period start date (YYYY-MM-DD)

• spike_end (string, required): Spike period end date (YYYY-MM-DD)

• workgroup (string, optional): Workgroup filter for PostgreSQL query

Example - Using CSV:

{
  "csv_file": "./reports/athena_ETL_2025-11-08_to_2025-11-27.csv",
  "baseline_start": "2025-11-08",
  "baseline_end": "2025-11-11",
  "spike_start": "2025-11-12",
  "spike_end": "2025-11-27"
}

Example - Using PostgreSQL:

{
  "baseline_start": "2025-11-08",
  "baseline_end": "2025-11-11",
  "spike_start": "2025-11-12",
  "spike_end": "2025-11-27",
  "workgroup": "ETL"
}

Returns:

• Summary statistics

• Daily metrics comparison

• Period comparison (baseline vs spike)

• Query pattern analysis

• Top expensive queries

• New query patterns identified

3. compare_expensive_queries

Compares expensive queries and extracts patterns. Can query from PostgreSQL database or read from CSV file.

Parameters:

• csv_file (string, optional): Path to CSV file with query data. If not provided, queries PostgreSQL

• query_pattern (string, optional): Pattern to filter queries (e.g., table name)

• query_id (string, optional): Specific query execution ID to analyze

• baseline_start (string, optional): Baseline start date for comparison (YYYY-MM-DD)

• baseline_end (string, optional): Baseline end date for comparison (YYYY-MM-DD)

• target_date (string, optional): Target date for comparison (YYYY-MM-DD)

• start_date (string, optional): Start date for PostgreSQL query (YYYY-MM-DD, required if csv_file not provided)

• end_date (string, optional): End date for PostgreSQL query (YYYY-MM-DD, required if csv_file not provided)

• workgroup (string, optional): Workgroup filter for PostgreSQL query

Example - Using CSV:

{
  "csv_file": "./reports/athena_ETL_2025-12-10_to_2025-12-16.csv",
  "query_pattern": "parquet__all_crm_users",
  "baseline_start": "2025-12-10",
  "baseline_end": "2025-12-14",
  "target_date": "2025-12-15"
}

Example - Using PostgreSQL:

{
  "start_date": "2025-12-10",
  "end_date": "2025-12-16",
  "query_pattern": "parquet__all_crm_users",
  "baseline_start": "2025-12-10",
  "baseline_end": "2025-12-14",
  "target_date": "2025-12-15",
  "workgroup": "ETL"
}

Returns:

• Query details with extracted features

• Statistical comparisons

• Pattern analysis by source table and date ranges

Project Structure

mcp-aws-cost/
├── src/
│   ├── __init__.py
│   ├── server.py              # Main MCP server entry point
│   ├── tools/
│   │   ├── __init__.py
│   │   ├── fetch_queries.py   # Fetch queries from AWS Athena
│   │   ├── analyze_cost.py    # Analyze cost increases
│   │   └── compare_queries.py # Compare expensive queries
│   └── utils/
│       ├── __init__.py
│       ├── query_parser.py     # Query pattern extraction utilities
│       └── report_formatter.py # Report formatting utilities
├── reports/                    # Directory for CSV exports (gitignored)
├── requirements.txt
├── README.md
└── pyproject.toml

Dependencies

• mcp: MCP Python SDK

• boto3: AWS SDK for Python

• pandas: Data analysis library

• python-dateutil: Date parsing utilities

AWS Permissions

The server requires the following AWS permissions:

• athena:ListQueryExecutions - List query executions in workgroup

• athena:BatchGetQueryExecution - Get query execution details

• athena:GetQueryExecution - Get individual query execution details

Error Handling

The server includes comprehensive error handling:

• Validates date formats (YYYY-MM-DD)

• Handles missing CSV files gracefully

• Provides clear error messages for AWS API failures

• Validates query IDs and patterns exist in data

License

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Contributing

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