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All four interfaces call the same API. The difference is how you invoke it.

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Decision tree

Do you have a coding agent? (Claude Code, Claude Desktop, Cursor, Windsurf) → Yes → MCP server. The agent calls tools directly. No shell, no files. Do you want file-based, version-controlled rules? → Yes → CLI. Sources and tests live in sources/ and tests/, committed alongside your code (.aethis/ holds tool state). Run aethis test in CI. Are you shipping a Python service that calls Aethis? → Yes → Python SDK. Sync and async clients, typed Pydantic models, a stateful DecisionSession for wizard flows, and narrow generation recovery methods. Are you integrating from another stack (Node, Go, Rust, …) or want zero install? → Yes → REST API. Any HTTP client. Decision endpoints on leaf rulesets are safe to call client-side (no key required).

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Comparison

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MCP server

Use when: your authoring and evaluation happen inside a coding agent session. The agent reads your policy document, runs aethis_create_ruleset, iterates until tests pass, and publishes — without you touching a shell. Concrete scenario: You paste a benefits policy document into Claude Code and ask it to author rules. The agent calls aethis_discover_sections, creates rulesets for each section, runs aethis_generate_and_test, refines based on failures, and publishes — with you reviewing output and providing domain feedback. MCP server overview →

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CLI

Use when: you want source documents, guidance, and test cases version-controlled as files alongside your codebase. CI pipelines run aethis test on pull requests to catch regressions. Local authoring happens in a terminal. Concrete scenario: A compliance team maintains eligibility rules in a Git repo. When legislation changes, they update sources/ and tests/scenarios.yaml, open a PR, CI runs aethis test, and the reviewer approves before publishing. CLI reference →

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Python SDK

Use when: you’re shipping a Python service that calls Aethis from a server context — FastAPI, Django, a Celery worker, a notebook. The SDK provides typed Pydantic response models, sync and async clients, a stateful DecisionSession adapter for wizard / chatbot intake, and narrow methods to inspect or explicitly cancel an authoring generation job. Use the CLI or MCP server for the full authoring workflow. Concrete scenario: A FastAPI eligibility service holds one AsyncAethis instance per process, exposes /eligibility/free-school-meals, and forwards the typed DecideResponse envelope (with decision_id, inputs_hash, and trace) to the caller. The decision path is fully async, key-pooled, and pre-typed for IDE autocompletion. Python SDK reference →

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REST API

Use when: you’re building a product that integrates Aethis — a mortgage pre-qualification backend that calls POST /decide in real time, a compliance dashboard that shows GET /schema for each active ruleset, or a custom authoring pipeline that drives the API directly. Decision endpoints are safe to call client-side (no API key required). Authoring endpoints require a key and should be called from your server. Concrete scenario: A lending application calls POST /api/v1/public/decide with applicant data on every form submission. Under 1ms per call in the engine, no infrastructure, full audit trail stored in the response. REST API →