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Python capability compiler: read-only analysis, experiment history/comparison and exposure planning.
About
Python capability compiler: read-only analysis, experiment history/comparison and exposure planning.
Security Report
Valid MCP server (2 strong, 1 medium validity signals). No known CVEs in dependencies. ⚠️ Package registry links to a different repository than scanned source. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
15 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-alien6-studio-outerspace-apizr": {
"args": [
"outerspace-apizr-mcp"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
OuterSpace Apizr
Turn existing Python functions into REST APIs or MCP tools. Inspect training scripts and notebooks, record experiments, and compare their results.
Apizr 0.4.6 supports Python 3.11–3.15 on macOS and Linux.
Install
With uv:
uv tool install outerspace-apizr==0.4.6
apizr --version
Prefer pip or Homebrew? Follow the installation guide.
Try it
Save this as pricing.py:
def total(quantity: int, unit_price: float) -> float:
return round(quantity * unit_price, 2)
Inspect the function, then generate its MCP server:
apizr inspect pricing.py
apizr generate mcp pricing.py --select total --output-dir dist/mcp
Apizr reads the function without running it. You choose which functions to expose. The generated server runs them when a client calls them.
Follow the Quickstart to start a server and make your first REST or MCP calls. The full walkthrough explains how this works across a project.
Work with experiments
Start with a training script or notebook you want to understand:
apizr experiment inspect train.py
The report shows the data, parameters and calculations found in the source. It does not train a model or invent scores.
You can then run trusted code, keep its history, compare results and share a selected prediction function. Start with the Data Scientist quickstart, or follow the Academy.
Go further
- Track Python calls and Jupyter cells.
- Review experiments in VS Code.
- Analyze a project from an MCP client.
- Build a service image or deliver it with Attest.
The core handles inspection and generation. Plugins are optional. Run generated services and experiments only with code and dependencies you trust.
Documentation · Release notes · Contributing · Report an issue
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