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MCP server for Steampipe data queries
This MCP server plugs Steampipe into AI assistants so they can query cloud and SaaS data in natural language. It works with a local Steampipe install or a Turbot Pipes workspace, and all access is read-only.
Builders who want Claude Desktop, Cursor, or another MCP client to query Steampipe data.
You can ask your assistant to inspect cloud resources, review security posture, and draft SQL without leaving the chat.
What it does
SQL query tool
Runs read-only PostgreSQL queries against Steampipe data with `steampipe_query`.
Table discovery tools
Lists available tables with `steampipe_table_list` and shows column details with `steampipe_table_show`.
Plugin inspection tools
Lists installed Steampipe plugins and shows details for a specific plugin with `steampipe_plugin_list` and `steampipe_plugin_show`.
Best practices prompt
Includes a `best_practices` prompt to teach the model how to work with Steampipe data.
Connection status resource
Exposes a `status` resource with the current connection string and connected state.
How to get it
- 1Clone the repository and navigate to the directory
git clone https://github.com/turbot/steampipe-mcp.git cd steampipe-mcp
- 2Install dependencies
npm install
- 3Build the project
npm run build
README
Steampipe Model Context Protocol (MCP) Server
Unlock the power of AI-driven infrastructure analysis with Steampipe! This Model Context Protocol server seamlessly connects AI assistants like Claude to your cloud infrastructure data, enabling natural language exploration and analysis of your entire cloud estate.
Steampipe MCP bridges AI assistants and your infrastructure data, allowing natural language:
- Queries across AWS, Azure, GCP and 100+ cloud services
- Security and compliance analysis
- Cost and resource optimization
- Query development assistance
Works with both local Steampipe installations and Turbot Pipes workspaces, providing safe, read-only access to all your cloud and SaaS data.
Installation
Prerequisites
- Node.js v16 or higher (includes
npx) - For local use: Steampipe installed and running (
steampipe service start) - For Turbot Pipes: A Turbot Pipes workspace and connection string
Configuration
Add Steampipe MCP to your AI assistant's configuration file:
{
"mcpServers": {
"steampipe": {
"command": "npx",
"args": [
"-y",
"@turbot/steampipe-mcp"
]
}
}
}
By default, this connects to your local Steampipe installation at postgresql://steampipe@localhost:9193/steampipe. Make sure to run steampipe service start first.
To connect to a Turbot Pipes workspace instead, add your connection string to the args:
{
"mcpServers": {
"steampipe": {
"command": "npx",
"args": [
"-y",
"@turbot/steampipe-mcp",
"postgresql://my_name:my_pw@workspace-name.usea1.db.pipes.turbot.com:9193/abc123"
]
}
}
}
AI Assistant Setup
| Assistant | Config File Location | Setup Guide |
|---|---|---|
| Claude Desktop | claude_desktop_config.json | Claude Desktop MCP Guide → |
| Cursor | ~/.cursor/mcp.json | Cursor MCP Guide → |
Save the configuration file and restart your AI assistant for the changes to take effect.
Prompting Guide
First, run the best_practices prompt included in the MCP server to teach your LLM how best to work with Steampipe. Then, ask anything!
Explore your cloud infrastructure:
What AWS accounts can you see?
Simple, specific questions work well:
Show me all S3 buckets that were created in the last week
Generate infrastructure reports:
List my EC2 instances with their attached EBS volumes
Dive into security analysis:
Find any IAM users with access keys that haven't been rotated in the last 90 days
Get compliance insights:
Show me all EC2 instances that don't comply with our tagging standards
Explore potential risks:
Analyze my S3 buckets for security risks including public access, logging, and encryption
Remember to:
- Be specific about which cloud resources you want to analyze (EC2, S3, IAM, etc.)
- Mention regions or accounts if you're interested in specific ones
- Start with simple queries before adding complex conditions
- Use natural language - the LLM will handle the SQL translation
- Be bold and exploratory - the LLM can help you discover insights across your entire infrastructure!
Capabilities
Tools
-
steampipe_query
- Query cloud and security logs with SQL.
- For best performance: use CTEs instead of joins, limit columns requested.
- All queries are read-only and use PostgreSQL syntax.
- Input:
sql(string): The SQL query to execute using PostgreSQL syntax
-
steampipe_table_list
- List all available Steampipe tables.
- Optional input:
schema(string): Filter tables by specific schema - Optional input:
filter(string): Filter tables by ILIKE pattern (e.g. '%ec2%')
-
steampipe_table_show
- Get detailed information about a specific table, including column definitions, data types, and descriptions.
- Input:
name(string): The name of the table to show details for (can be schema qualified e.g. 'aws_account' or 'aws.aws_account') - Optional input:
schema(string): The schema containing the table
-
steampipe_plugin_list
- List all Steampipe plugins installed on the system. Plugins provide access to different data sources like AWS, GCP, or Azure.
- No input parameters required
-
steampipe_plugin_show
- Get details for a specific Steampipe plugin installation, including version, memory limits, and configuration.
- Input:
name(string): Name of the plugin to show details for
Prompts
- best_practices
- Best practices for working with Steampipe data
- Provides detailed guidance on:
- Response style and formatting conventions
- Using CTEs (WITH clauses) vs joins
- SQL syntax and style conventions
- Column selection and optimization
- Schema exploration and understanding
- Query structure and organization
- Performance considerations and caching
- Error handling and troubleshooting
Resources
- status
- Represents the current state of the Steampipe connection
- Properties include:
- connection_string: The current database connection string
- status: The connection state (connected/disconnected)
This resource enables AI tools to check and verify the connection status to your Steampipe instance.
Development
Clone and Setup
- Clone the repository and navigate to the directory:
git clone https://github.com/turbot/steampipe-mcp.git
cd steampipe-mcp
- Install dependencies:
npm install
- Build the project:
npm run build
Testing
To test your local development build with AI tools that support MCP, update your MCP configuration to use the local dist/index.js instead of the npm package. For example:
{
"mcpServers": {
"steampipe": {
"command": "node",
"args": [
"/absolute/path/to/steampipe-mcp/dist/index.js",
"postgresql://steampipe@localhost:9193/steampipe"
]
}
}
}
Or, use the MCP Inspector to validate the server implementation:
npx @modelcontextprotocol/inspector dist/index.js
Environment Variables
The following environment variables can be used to configure the MCP server:
STEAMPIPE_MCP_LOG_LEVEL: Control server logging verbosity (default:info)STEAMPIPE_MCP_WORKSPACE_DATABASE: Override the default Steampipe connection string (default:postgresql://steampipe@localhost:9193/steampipe)
Open Source & Contributing
This repository is published under the Apache 2.0 license. Please see our code of conduct. We look forward to collaborating with you!
Steampipe is a product produced from this open source software, exclusively by Turbot HQ, Inc. It is distributed under our commercial terms. Others are allowed to make their own distribution of the software, but they cannot use any of the Turbot trademarks, cloud services, etc. You can learn more in our Open Source FAQ.
Get Involved
Want to help but don't know where to start? Pick up one of the help wanted issues:
Files in the repo
- src
- .gitignore
- CHANGELOG.md
- LICENSE
- package-lock.json
- package.json
- README.md
- tsconfig.json
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