Enables AI agents and their users to ask business questions in plain language and receive governed metric values, breakdowns, share-of-total figures, and the exact parameterized SQL behind them, drawn from a single semantic model rather than ad-hoc SQL. It also lets clients inspect available metrics and their definitions, and preview optimized queries without executing them.
Open-source agentic schema layer. Define metrics once in YAML, query governed data from any warehouse (Snowflake, BigQuery, Databricks, PostgreSQL, DuckDB) via MCP.
A read-only Model Context Protocol server over Google BigQuery that lets AI clients answer plain-language data questions by discovering schemas, inspecting table freshness and code assets, and running validated SELECT queries under the user's own Google credentials.
Enables AI assistants to query trusted BigQuery analytics using curated Business Intelligence tools, providing secure, accurate business metrics and KPIs through natural language.
Exposes a governed semantic layer to AI agents so they select metrics and dimensions from a closed, engineer-declared vocabulary instead of writing SQL. Validates every typed call against the contract, rejects out-of-contract requests with structured errors and suggestions, and returns query rows together with the compiled SQL and dbt lineage for auditing.