Context Engineering Agent for generating and maintaining QueryData / Conversational Analytics API context sets (templates, facets, value searches) for natural language to SQL tasks. Works with AlloyDB, Cloud SQL for MySQL, Cloud SQL for PostgreSQL, and Spanner.
Plugin db-context-engineering from marketplace data-cloud-plugins in GoogleCloudPlatform/data-cloud-plugins. Commands read from the repo's manifest — 3 days ago.
This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.
autoctx-bootstrapGuides the agent to bootstrap an initial context set (templates & facets) by deducing key information from the database schema and generating a ContextSet file.
autoctx-dataset-generationGenerate and expand datasets of Natural Language Questions (NLQ) and SQL pairs for evaluation.
autoctx-evaluateGuides the agent to execute an evaluation of a generated ContextSet against a golden dataset utilizing the Evalbench framework.
autoctx-hillclimbGuides the agent to perform hill-climbing iterations to improve a ContextSet based on evaluation results.
autoctx-initOrchestrates the initialization workflow for auto context generation, and provides helper workflow for setting up dataset connection by creating or updating tools.yaml configurations.
context-generation-guideGuidelines and best practices for generating context items (Templates, Facets, Value Searches). Use this skill whenever the user asks to create, author, or generate context for database enrichment, or asks for examples and instructions on how to write templates, facets, or value searches. It helps b
db-context-engineeringruns: uvx [email protected]
toolboxruns: uvx [email protected] --config autoctx/tools.yaml --stdio
This plugin provides a specialized suite of skills for data engineers and database practitioners working on Google Cloud. It acts as an expert assistant, allowing you to use natural language prompts in your preferred coding agent to architect complex data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across GCP's data ecosystem.
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