maxcompute-semantic (mcs)

Give AI agents the context they need to query your MaxCompute data.

mcs is a CLI for building a local semantic package from your MaxCompute project: table and column descriptions, JOIN relationships, UDFs, business metrics, verified SQL, and notes from past queries. Your agent reads that package before it writes SQL, then uses mcs to review, cost-check, and execute the query.

Documentation · PyPI · Changelog

Why mcs?

AI agents can call MaxCompute, but they do not know your data warehouse. They guess table names, miss JOIN keys, and write SQL that runs but answers the wrong question.

mcs gives the agent a local source of truth:

• Semantic package - mcs build scans table metadata and produces a structured package the agent can read before writing SQL.
• Memory - verified queries, failed patterns, and domain notes accumulate over time. Similar questions can reuse known-good SQL instead of starting cold.
• SQL guard rails - cost estimation, write protection, dialect review, and tier-aware schema resolution run before the query reaches MaxCompute.
• Agent integration - the packaged skill works with Claude Code, Cursor, Codex, Gemini CLI, Qwen Code, OpenCode, and many other agent platforms.

How it works

You configure a profile → mcs build produces a semantic package → agent reads it to write SQL

• [A] profile - identity, compute project, cost thresholds, and table scope. One profile usually maps to one business scenario.
• [B] semantic package - local SQLite + markdown knowledge base produced by mcs build.
• [C] agent - connects through the generated skill; reads the package, runs mcs sql cost, and then runs mcs sql execute or the async SQL lifecycle.

Business scenario = profile + semantic package + metrics + query memory.

Quick Start

Before you start, have these ready: a MaxCompute project, region or endpoint, auth method (AK, keyless/NCS, or process), the tables or schemas you want to cover, and SELECT permission on those tables.

1. Install

uv tool install maxcompute-semantic
mcs --version

Then register the skill with the agents on this machine:

mcs skill install --detect -g

Prefer an agent-assisted install? Tell any connected AI agent:

Install mcs for me, read this guide fully then follow step by step: curl -fsSL https://raw.githubusercontent.com/aliyun/maxcompute-semantic/main/scripts/install.md

The guide tells the agent to show you the exact command before running any remote bootstrap or final install step.

2. Create a profile

mcs profile create
mcs link bind <profile-name>

profile create opens an interactive wizard for endpoint, auth, project, sources, and cost thresholds. link bind ties the current directory to that profile so future commands can resolve it automatically.

3. Build the semantic package

mcs build
mcs doctor

4. Ask through your agent

"How did last month's order GMV compare year-over-year?"

The agent reads mcs show, checks the query with mcs sql review, estimates cost with mcs sql cost, and then runs mcs sql execute when the query is safe to run. Have it record working SQL so similar questions get BM25 recall next time:

mcs memory verify --question "How did last month's order GMV compare year-over-year?" --sql "SELECT ..." --tables your_project.your_schema.orders

You can also let the agent do steps 2 and 3. After installing the skill, describe your business scenario:

"I'm doing monthly analysis on warehouse A, mainly looking at the order and user tables in the dwd/dws layers of your_project; help me build the semantic layer."

The agent will guide profile setup, bind the working directory, and run mcs build.

Safety and privacy

• mcs stores profiles and semantic packages locally. The semantic package is SQLite + markdown, not a hosted service.
• Credentials stay in your profile configuration. You can store AK values as environment-variable references instead of literals.
• mcs build reads metadata and optional samples only for the sources you put in the profile.
• mcs sql cost, mcs sql review, and write protection are designed to catch expensive or unsafe SQL before execution.
• Agents use mcs as the gatekeeper. They do not need direct MaxCompute credentials outside the configured profile.

Key Features

Run mcs <command> --help for the full option surface.

Manual Install

uv tool install maxcompute-semantic    # recommended, Python >= 3.10
# in a virtualenv / managed Python: pip install maxcompute-semantic

If ~/.local/bin is not on your PATH:

echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc && source ~/.zshrc

Then register the skill with your agents. Choose one:

mcs skill install --detect -g  # recommended: only agents found on this machine
mcs skill install --all -g     # every supported agent platform

Upgrade / Uninstall

The uninstall example uses -g because the install examples above are global; omit it for local skill installs.

mcs update                                    # check for latest and upgrade
mcs skill uninstall --all -g                  # remove global skill symlinks
uv tool uninstall maxcompute-semantic         # remove CLI

Configuration

Profiles store auth, compute project, data sources, and cost thresholds:

mcs profile create                    # interactive wizard
mcs profile create --from-file @p.yaml  # scripted
mcs link bind <name>                  # bind cwd to profile

Profile resolution order: --profile flag → MCS_PROFILE env → cwd binding → ALIBABA_CLOUD_* / MAXCOMPUTE_* env vars.

Contributing

uv sync --extra dev
uv run pytest tests/ -m 'not live'
uv run pytest tests/ -m 'not live' --cov=maxcompute_semantic --cov-report=xml --cov-fail-under=85
uv run diff-cover coverage.xml --compare-branch=origin/main --fail-under=80
uv run ruff check src/ tests/
uv run mypy src/

License

Apache License 2.0 - see LICENSE. Third-party notices in NOTICE.