QuickSight MCP Server

A Model Context Protocol (MCP) server for Amazon QuickSight that enables AI assistants to create and manage QuickSight dashboards, analyses, datasets, and data sources.

Features

🔍 Discovery Tools (9 Read Operations)

quicksight_overview - Get account-wide QuickSight statistics
list_* - List all datasets, data sources, analyses, dashboards
describe_* - Get detailed information about any resource

📝 Write Operations (19 Tools)

Data Sources (3 tools)

Create and update data sources (RDS, Redshift, S3, Athena, etc.)
Manage data source permissions

Datasets (3 tools)

Create datasets with physical/logical tables
Update schemas and transformations
Manage dataset permissions

Analyses (3 tools)

Build analyses with visuals, filters, parameters
Update analysis definitions
Control analysis access

Dashboards (4 tools)

Create dashboards from analyses
Update and publish dashboard versions
Manage viewer/editor permissions

Data Ingestion (6 tools)

Trigger SPICE data refresh
Monitor ingestion jobs
Create and manage refresh schedules

Total: 28 tools for complete QuickSight management

Installation

Prerequisites

Python 3.11 or higher
AWS credentials configured
Amazon QuickSight account

Setup

Install dependencies:

pip install -e .

# For HTTP/SSE server support:
pip install -e ".[server]"

Configure environment:

cp .env.example .env
# Edit .env with your AWS credentials

Required .env variables:

AWS_ACCOUNT_ID=123456789012
AWS_REGION=us-east-1
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key

Usage

STDIO Transport (for Claude Desktop)

python main.py

Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "quicksight": {
      "command": "python",
      "args": ["/path/to/quicksight-mcp/main.py"],
      "env": {
        "AWS_ACCOUNT_ID": "123456789012",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

SSE Transport (for web clients)

python main.py --transport sse --host 0.0.0.0 --port 8080

HTTP Transport (RESTful API)

python main.py --transport http --port 3000

Debug Mode

python main.py --debug

Available Tools

Discovery Tools

Tool	Description
`quicksight_overview`	Get overview statistics of all resources
`list_dashboards`	List all dashboards with IDs and names
`list_analyses`	List all analyses with IDs and names
`list_datasets`	List all datasets with IDs and names
`list_datasources`	List all data sources with IDs and names
`describe_dashboard`	Get detailed dashboard information
`describe_analysis`	Get detailed analysis definition
`describe_dataset`	Get dataset schema and configuration
`describe_datasource`	Get data source connection details

Example Prompts for Claude

"Show me all my QuickSight dashboards"

"What datasets are available in my account?"

"Describe the schema of dataset xyz-123"

"Give me an overview of my QuickSight resources"

Project Structure

quicksight-mcp/
├── main.py                          # Entry point with CLI
├── pyproject.toml                   # Project configuration
├── .env.example                     # Environment template
│
├── quicksight_mcp/                  # Main package
│   ├── __init__.py
│   ├── config.py                    # Configuration management
│   ├── server.py                    # MCP server setup
│   ├── service.py                   # QuickSight API wrapper
│   │
│   ├── tools/                       # MCP tools
│   │   ├── __init__.py
│   │   ├── discovery.py            # Read operations
│   │   ├── datasource.py           # Data source tools (coming)
│   │   ├── dataset.py              # Dataset tools (coming)
│   │   ├── analysis.py             # Analysis tools (coming)
│   │   └── dashboard.py            # Dashboard tools (coming)
│   │
│   └── models/                      # Data models
│       └── __init__.py

Development

Adding New Tools

Create a new file in quicksight_mcp/tools/
Define your tool functions
Register them in the tool module
Import and register in main.py

Example:

# quicksight_mcp/tools/my_tools.py
def register_my_tools(mcp):
    @mcp.tool(name="my_tool")
    async def my_tool(param: str) -> dict:
        config = mcp.config
        service = QuickSightService(config.aws_account_id, config.aws_region)
        # Your logic here
        return {}

Architecture

Multi-Transport Support

STDIO: For Claude Desktop integration
SSE: For streaming web clients
HTTP: For RESTful API access

Configuration Management

Environment variables from .env file
CLI argument overrides
AWS credentials support

QuickSight Service Layer

Clean boto3 wrapper
Error handling and logging
Pagination support for large results

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

License

This project is open source and available under the MIT License.

Roadmap

Support

For questions or issues, please open an issue on GitHub.

`quicksight_overview`

Provides a comprehensive overview of QuickSight resources in the account.

Parameters:
- account_id (required): AWS account ID
- region (optional): AWS region (default: us-east-1)

Usage Examples

Basic Resource Listing

# List all dashboards
q chat "List all QuickSight dashboards in account 123456789012"

# List analyses in a specific region
q chat "Show me all analyses in us-west-2 for account 123456789012"

Account Overview

# Get comprehensive QuickSight statistics
q chat "Give me an overview of QuickSight resources in account 123456789012"

Prerequisites

AWS credentials configured with appropriate QuickSight permissions
Access to the target AWS account and regions
QuickSight service activated in the target regions

Required AWS Permissions

The MCP server requires the following QuickSight permissions:

quicksight:ListDashboards
quicksight:ListAnalyses
quicksight:ListDataSets
quicksight:ListDataSources
quicksight:DescribeDashboard
quicksight:DescribeAnalysis
quicksight:DescribeDataSet
quicksight:DescribeDataSource

Installation and Configuration

Install UV on macOS/Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

create venv and

% git clone https://github.com/heisenbergye/quicksight-mcp.git
% cd quicksight-mcp
% uv venv .venv
% source .venv/bin/activate
% uv add "mcp[cli]" boto3

Install Q CLI and Ensure the MCP server is properly configured in your Q CLI environment, or you can use CLINE in IDE etc.

$ cat ~/.aws/amazonq/mcp.json
{
  "mcpServers": {
    "quicksight-mcp": {
      "command": "/Users/xxxxx/xxxxx/Cline/MCP/quicksight-mcp/.venv/bin/python3",
      "args": [
        "/Users/xxxxx/xxxxx/Cline/MCP/quicksight-mcp/main.py"
      ],
      "disabled": false,
      "alwaysAllow": []
    }
}
}

Configue AWS CLi with AWS credentials have the necessary QuickSight permissions
Test connectivity with a simple resource listing command

Common Use Cases

Data Governance

Identify impact of data source changes
Audit data dependencies across your organization

Resource Management

Inventory all QuickSight resources
Identify unused or orphaned resources
Plan resource migrations or cleanup

Troubleshooting

Diagnose dashboard or analysis issues by examining dependencies
Understand data flow when reports show unexpected results
Validate data source configurations

Error Handling

The server provides detailed error messages for common issues:

Invalid account IDs or resource IDs
Insufficient permissions
Resources not found
Region-specific access issues

Support

For issues or questions about this MCP server, please refer to the MCP documentation or contact your system administrator.

QuickSight MCP Server

A Model Context Protocol (MCP) server for Amazon QuickSight that enables AI assistants to create and manage QuickSight dashboards, analyses, datasets, and data sources.

Features

🔍 Discovery Tools (9 Read Operations)

quicksight_overview - Get account-wide QuickSight statistics
list_* - List all datasets, data sources, analyses, dashboards
describe_* - Get detailed information about any resource

📝 Write Operations (19 Tools)

Data Sources (3 tools)

Create and update data sources (RDS, Redshift, S3, Athena, etc.)
Manage data source permissions

Datasets (3 tools)

Create datasets with physical/logical tables
Update schemas and transformations
Manage dataset permissions

Analyses (3 tools)

Build analyses with visuals, filters, parameters
Update analysis definitions
Control analysis access

Dashboards (4 tools)

Create dashboards from analyses
Update and publish dashboard versions
Manage viewer/editor permissions

Data Ingestion (6 tools)

Trigger SPICE data refresh
Monitor ingestion jobs
Create and manage refresh schedules

Total: 28 tools for complete QuickSight management

Installation

Prerequisites

Python 3.11 or higher
AWS credentials configured
Amazon QuickSight account

Setup

Install dependencies:

pip install -e .

# For HTTP/SSE server support:
pip install -e ".[server]"

Configure environment:

cp .env.example .env
# Edit .env with your AWS credentials

Required .env variables:

AWS_ACCOUNT_ID=123456789012
AWS_REGION=us-east-1
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key

Usage

STDIO Transport (for Claude Desktop)

python main.py

Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "quicksight": {
      "command": "python",
      "args": ["/path/to/quicksight-mcp/main.py"],
      "env": {
        "AWS_ACCOUNT_ID": "123456789012",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

SSE Transport (for web clients)

python main.py --transport sse --host 0.0.0.0 --port 8080

HTTP Transport (RESTful API)

python main.py --transport http --port 3000

Debug Mode

python main.py --debug

Available Tools

Discovery Tools

Tool	Description
`quicksight_overview`	Get overview statistics of all resources
`list_dashboards`	List all dashboards with IDs and names
`list_analyses`	List all analyses with IDs and names
`list_datasets`	List all datasets with IDs and names
`list_datasources`	List all data sources with IDs and names
`describe_dashboard`	Get detailed dashboard information
`describe_analysis`	Get detailed analysis definition
`describe_dataset`	Get dataset schema and configuration
`describe_datasource`	Get data source connection details

Example Prompts for Claude

"Show me all my QuickSight dashboards"

"What datasets are available in my account?"

"Describe the schema of dataset xyz-123"

"Give me an overview of my QuickSight resources"

Project Structure

quicksight-mcp/
├── main.py                          # Entry point with CLI
├── pyproject.toml                   # Project configuration
├── .env.example                     # Environment template
│
├── quicksight_mcp/                  # Main package
│   ├── __init__.py
│   ├── config.py                    # Configuration management
│   ├── server.py                    # MCP server setup
│   ├── service.py                   # QuickSight API wrapper
│   │
│   ├── tools/                       # MCP tools
│   │   ├── __init__.py
│   │   ├── discovery.py            # Read operations
│   │   ├── datasource.py           # Data source tools (coming)
│   │   ├── dataset.py              # Dataset tools (coming)
│   │   ├── analysis.py             # Analysis tools (coming)
│   │   └── dashboard.py            # Dashboard tools (coming)
│   │
│   └── models/                      # Data models
│       └── __init__.py

Development

Adding New Tools

Create a new file in quicksight_mcp/tools/
Define your tool functions
Register them in the tool module
Import and register in main.py

Example:

# quicksight_mcp/tools/my_tools.py
def register_my_tools(mcp):
    @mcp.tool(name="my_tool")
    async def my_tool(param: str) -> dict:
        config = mcp.config
        service = QuickSightService(config.aws_account_id, config.aws_region)
        # Your logic here
        return {}

Architecture

Multi-Transport Support

STDIO: For Claude Desktop integration
SSE: For streaming web clients
HTTP: For RESTful API access

Configuration Management

Environment variables from .env file
CLI argument overrides
AWS credentials support

QuickSight Service Layer

Clean boto3 wrapper
Error handling and logging
Pagination support for large results

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

License

This project is open source and available under the MIT License.

Roadmap

Support

For questions or issues, please open an issue on GitHub.

`quicksight_overview`

Provides a comprehensive overview of QuickSight resources in the account.

Parameters:
- account_id (required): AWS account ID
- region (optional): AWS region (default: us-east-1)

Usage Examples

Basic Resource Listing

# List all dashboards
q chat "List all QuickSight dashboards in account 123456789012"

# List analyses in a specific region
q chat "Show me all analyses in us-west-2 for account 123456789012"

Account Overview

# Get comprehensive QuickSight statistics
q chat "Give me an overview of QuickSight resources in account 123456789012"

Prerequisites

AWS credentials configured with appropriate QuickSight permissions
Access to the target AWS account and regions
QuickSight service activated in the target regions

Required AWS Permissions

The MCP server requires the following QuickSight permissions:

quicksight:ListDashboards
quicksight:ListAnalyses
quicksight:ListDataSets
quicksight:ListDataSources
quicksight:DescribeDashboard
quicksight:DescribeAnalysis
quicksight:DescribeDataSet
quicksight:DescribeDataSource

Installation and Configuration

Install UV on macOS/Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

create venv and

% git clone https://github.com/heisenbergye/quicksight-mcp.git
% cd quicksight-mcp
% uv venv .venv
% source .venv/bin/activate
% uv add "mcp[cli]" boto3

Install Q CLI and Ensure the MCP server is properly configured in your Q CLI environment, or you can use CLINE in IDE etc.

$ cat ~/.aws/amazonq/mcp.json
{
  "mcpServers": {
    "quicksight-mcp": {
      "command": "/Users/xxxxx/xxxxx/Cline/MCP/quicksight-mcp/.venv/bin/python3",
      "args": [
        "/Users/xxxxx/xxxxx/Cline/MCP/quicksight-mcp/main.py"
      ],
      "disabled": false,
      "alwaysAllow": []
    }
}
}

Configue AWS CLi with AWS credentials have the necessary QuickSight permissions
Test connectivity with a simple resource listing command

Common Use Cases

Data Governance

Identify impact of data source changes
Audit data dependencies across your organization

Resource Management

Inventory all QuickSight resources
Identify unused or orphaned resources
Plan resource migrations or cleanup

Troubleshooting

Diagnose dashboard or analysis issues by examining dependencies
Understand data flow when reports show unexpected results
Validate data source configurations

Error Handling

The server provides detailed error messages for common issues:

Invalid account IDs or resource IDs
Insufficient permissions
Resources not found
Region-specific access issues

Support

For issues or questions about this MCP server, please refer to the MCP documentation or contact your system administrator.