Nvidia Ontology Query

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Auto Ontology grounded queries

Purpose

Query the current Auto Ontology implementation from an agent without prescribing a harness, SDK, or LangGraph template. Typed workflow artifacts, statuses, gates, and handoffs are in runtime-contract.yaml.

Prerequisites

Use a running Auto Ontology deployment and an authenticated MCP or REST client. If Auto Ontology is not running, use nvidia-ontology-setup first.

Limitations

This skill reads and validates answers. It does not expose FastAPI directly, write semantic definitions, publish governed results, or provide a raw-schema browser. Hand mutations and publication to nvidia-ontology-management.

Instructions

1. Call readiness before asking a question.
2. Discover terms and confirm answerability through the semantic layer.
3. Ask through MCP, or use the authenticated Next.js REST gateway as fallback.
4. Validate material intent, SQL, rows, truncation, and prose before presenting a claim-bearing answer.

Prefer MCP for reads

When the harness speaks MCP, use it. The handshake tool list is the live source of truth (mcp/auto_ontology_mcp/tools.py). Do not copy a tool table into the session; if the handshake and this skill disagree, trust the handshake.

Sequence (the server also advertises this so models do not jump straight to ask_question):

1. check_readiness — compiled semantic layer and a live DB connection.
2. search_terms — what the nouns mean here.
3. check_answerable — cheap coverage check.
4. ask_question — full text-to-SQL; tens of seconds; answer + SQL + rows (capped at 100 rows, with row_count / truncated).
5. For a claim-bearing answer, validate material intent, generated SQL, returned rows, truncation, and prose using query-validation.md.

Everything on MCP reads. There is no MCP tool for raw databases, schemas, or columns on purpose. describe_table is the legitimate table view: terms and SQL attributes the table participates in.

Connect: AUTO_ONTOLOGY_API_URL is the web app. Run auto-ontology-mcp, point the client at …/mcp, user signs in. No token on the MCP server. Details: repository mcp/README.md and docs/mcp.md.

REST fallback and write handoff

When MCP is unavailable, public clients call the authenticated Next.js gateway, not FastAPI :3001. FastAPI trusts x-auto-ontology-user-id and must not be exposed. A direct FastAPI request without conversation_id is stateless; with conversation_id it requires that internal header.

Auth (any of these; resolved in one place):

• Browser session cookie
• API token: x-api-key: $AUTO_ONTOLOGY_API_TOKEN (Authorization: Bearer works for auto_ontology_… tokens too). Token acts as its owner.
• OAuth bearer issued by Auto Ontology (MCP sign-in)
• SSO id token (Authorization: Bearer <jwt>), e.g. AI-Q — see stack.md

Shapes: docs/openapi/auto-ontology-api.json. Do not scrape all ~87 operations. A write request hands off to nvidia-ontology-management; this skill does not turn a read-only question into a mutation.

Chat

POST /api/chat/completions — SSE step / result / error / charts, then [DONE]. Permission chat:use; a request with conversation_id also needs conversation:write, or it gets 403.

• Omit conversation_id for a one-shot (no history, no chart step).
• Supply a client-generated UUID to create or continue a thread (needs chat:use and conversation:write).
• Wait for [DONE] before the next turn; overlapping requests return 409 Conversation in progress.
• A 404 on a conversation id means it belongs to another user.

Python (from the Auto Ontology README; no extra SDK):

import os
import requests

session = requests.Session()
session.headers["x-api-key"] = os.environ["AUTO_ONTOLOGY_API_TOKEN"]

terms = session.get("https://ontology.example.com/api/terms").json()

answer = session.post(
    "https://ontology.example.com/api/chat/completions",
    json={"question": "How many orders shipped last week?"},
)
# response is SSE, not a single JSON object

Open an API-created thread in the UI at /chat?focus=<conversation_id> when it belongs to the signed-in user.

Discovery (semantic layer, not a catalog browser)

MCP first. REST fallback:

• GET /api/terms (query, skip, limit)
• GET /api/terms/{term_id}
• GET /api/exploration/tables/{table_id}/details
• GET /api/exploration/graph

Writes and compile reset: nvidia-ontology-management.

Validate claim-bearing answers

Do not treat successful execution as sufficient evidence. Before presenting a decision-facing result, apply query-validation.md to check population, measure, unit, grain, joins, lineage, status, validity, rows, truncation, and answer prose. If a material constraint cannot be verified, return the gap rather than a stronger claim.

Empty or failed answers

ask_question / chat returned nothing useful → check_readiness (or GET /api/semantic-compilation/status) before rewriting the question. A named database is not proof SQL can execute. See nvidia-ontology-setup troubleshooting.

Examples

• For a grounded count, run check_readiness, search_terms, check_answerable, and then ask_question.
• If a response is truncated or violates measure grain, refuse the complete claim and follow query-validation.md.

Troubleshooting

For empty or failed answers, check readiness and compilation status before rephrasing. For REST errors, verify the public web origin, authentication, and conversation ownership before retrying.

See also

• stack.md — AI-Q, Nemotron, cuOpt (what exists vs what partners wire)
• query-validation.md — material intent, SQL, rows, and answer checks
• nvidia-ontology-management — model, modify, and publish through the layer
• nvidia-ontology-setup — bring-up and MCP OAuth discovery