by saucelabs
Analyze test data on Sauce Labs, including urine test results, urinary analysis results, and blood test vitamin B12 for
Integrates with Sauce Labs cloud testing platform to manage test jobs, browse devices, analyze results, and monitor testing infrastructure through API calls.
Sauce Labs is an official MCP server published by saucelabs that provides AI assistants with tools and capabilities via the Model Context Protocol. Analyze test data on Sauce Labs, including urine test results, urinary analysis results, and blood test vitamin B12 for It is categorized under cloud infrastructure.
You can install Sauce Labs 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.
Apache-2.0
Sauce Labs is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
According to our notes, Sauce Labs benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Sauce Labs has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Sauce Labs is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated Sauce Labs against two servers with overlapping tools; this profile had the clearer scope statement.
Useful MCP listing: Sauce Labs is the kind of server we cite when onboarding engineers to host + tool permissions.
Strong directory entry: Sauce Labs surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We evaluated Sauce Labs against two servers with overlapping tools; this profile had the clearer scope statement.
Sauce Labs is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Sauce Labs has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: Sauce Labs surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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A Model Context Protocol (MCP) server that provides comprehensive integration with Sauce Labs testing platform. This package includes two complementary MCP servers enabling AI assistants (LLM clients) to interact with Sauce Labs' device cloud, manage test jobs, analyze builds, and monitor testing infrastructure directly through natural language conversations.
This package provides two separate MCP servers optimized for different use cases:
sauce-api-mcp (Core Server) - Full Sauce Labs API integration for account management, device discovery, job analysis, builds, storage, and tunnels.
sauce-api-mcp-rdc (RDC OpenAPI Server) - Real Device Cloud (RDC) focused server with automated OpenAPI schema discovery and optimized for mobile device testing workflows.
Both servers can be configured simultaneously in your LLM client for comprehensive Sauce Labs integration.
pipInstall the package from PyPI:
pip install sauce-api-mcp
This will install both servers and make their command-line entry points available:
sauce-api-mcp (core server)sauce-api-mcp-rdc (RDC OpenAPI server)Verify installation:
which sauce-api-mcp
which sauce-api-mcp-rdc
Locate your Claude Desktop config file:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json~/.config/Claude/claude_desktop_config.jsonFind your Python installation's bin directory:
python3 -c "import sys; print(sys.prefix + '/bin')"
On macOS with system Python, this is typically:
/Library/Frameworks/Python.framework/Versions/3.12/bin
Add both servers to your config using the full paths from step 2:
{
"mcpServers": {
"sauce-api-mcp-core": {
"command": "/path/to/bin/sauce-api-mcp",
"env": {
"SAUCE_USERNAME": "your-sauce-username",
"SAUCE_ACCESS_KEY": "your-sauce-access-key"
}
},
"sauce-api-mcp-rdc": {
"command": "/path/to/bin/sauce-api-mcp-rdc",
"env": {
"SAUCE_USERNAME": "your-sauce-username",
"SAUCE_ACCESS_KEY": "your-sauce-access-key"
}
}
}
}
Restart the client to load the servers.
Add to ~/.gemini/settings.json:
{
"mcpServers": {
"sauce-api-mcp-core": {
"command": "/path/to/bin/sauce-api-mcp",
"env": {
"SAUCE_USERNAME": "your-sauce-username",
"SAUCE_ACCESS_KEY": "your-sauce-access-key"
}
}
}
}
Add to ~/.config/goose/config.yaml:
sauce-api-mcp-core:
cmd: /path/to/bin/sauce-api-mcp
description: Sauce Labs MCP (Core)
enabled: true
envs:
SAUCE_USERNAME: your-sauce-username
SAUCE_ACCESS_KEY: your-sauce-access-key
type: stdio
sauce-api-mcp-rdc:
cmd: /path/to/bin/sauce-api-mcp-rdc
description: Sauce Labs MCP (RDC)
enabled: true
envs:
SAUCE_USERNAME: your-sauce-username
SAUCE_ACCESS_KEY: your-sauce-access-key
type: stdio
Instead of adding credentials to config files, you can set them as environment variables:
export SAUCE_USERNAME="your-sauce-username"
export SAUCE_ACCESS_KEY="your-sauce-access-key"
Then omit the env block from your config. Both servers will automatically use these environment variables.
The entry point script isn't in your PATH. Verify installation and use the full path approach above:
python3 -m pip list | grep sauce-api-mcp
python3 -c "import sys; print(sys.prefix + '/bin/sauce-api-mcp')"
The MCP client is using a different Python environment than where you installed the package. Solutions:
Use full absolute path (recommended): Update your config to use the complete path shown by python3 -c "import sys; print(sys.prefix + '/bin/sauce-api-mcp')", not just which sauce-api-mcp
Install in system Python: If you're using a specific Python installation, ensure pip install uses that same Python
Alternative: Module invocation (less reliable):
"command": "python3",
"args": ["-m", "sauce_api_mcp.main"]
uv package manager (install uv)git clone https://github.com/saucelabs/sauce-api-mcp.git
cd sauce-api-mcp
uv sync
This creates a virtual environment and installs all dependencies in editable mode.
sauce-api-mcp/
├── src/sauce_api_mcp/
│ ├── __init__.py
│ ├── main.py # Core server entry point
│ ├── rdc_openapi.py # RDC OpenAPI server entry point
│ └── shared/ # Shared utilities
├── pyproject.toml # Package configuration & entry points
├── uv.lock # Locked dependencies
└── README.md
The pyproject.toml defines two console scripts:
[project.scripts]
sauce-api-mcp = "sauce_api_mcp.main:main"
sauce-api-mcp-rdc = "sauce_api_mcp.rdc_openapi:main"
When you run uv sync, these become available as commands in the virtual environment.
Activate the virtual environment (optional, uv commands work without it):
source .venv/bin/activate # On Windows: .venv\Scripts\activate
Run the servers locally (for testing):
uv run sauce-api-mcp
uv run sauce-api-mcp-rdc
Both will output: Error: This server is not meant to be run interactively — this is correct behavior (they're meant to be called by MCP clients).
Run with environment variables:
SAUCE_USERNAME=user SAUCE_ACCESS_KEY=key uv run sauce-api-mcp
Test with your MCP client:
{
"command": "/path/to/sauce-api-mcp/.venv/bin/sauce-api-mcp"
}
uv run pytest
uv run pytest -v # Verbose
uv run pytest src/sauce_api_mcp/tests/test_main.py # Specific test
uv add httpx
uv add --dev pytest-asyncio
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.