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A SQL agent needs more than a database schema to understand what the data means. The Open Knowledge Format (OKF) v0.2 offers a portable way to describe data and systems in Markdown files with YAML frontmatter; a connector or retrieval tool can make that context available to an agent. OKF does not itself retrieve documents, run SQL, or enforce database permissions, so treat the format, the tooling, and the execution safeguards as separate parts of the design.
What a knowledge layer adds to a database schema
A schema describes physical structure: tables, columns, and types. It may not explain that a field is a revenue measure, that a code maps to a business category, or that two tables should be joined on a particular key. A knowledge layer records those meanings and conventions in material an agent can consult when selecting tables and composing SQL.
The Open Knowledge Format v0.2 (OKF) is a representation for metadata, context, and curated insight about data and systems. Its specification says, “The format is intentionally minimal: a directory of markdown files with YAML frontmatter.” The format is designed to be readable, parseable, diffable, and portable—not to dictate a particular database agent architecture. Read the OKF v0.2 specification in the GoogleCloudPlatform knowledge-catalog repository.
Keep the format, tooling, and runtime separate
A useful design has three layers. Confusing them can lead to overestimating what an OKF bundle guarantees.
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1. The OKF representation
The bundle holds curated descriptions and context as Markdown with YAML frontmatter. The specification gives attention to provenance, trust, freshness, lifecycle, and attestation, while leaving implementation and runtime choices open. A bundle can document what a team believes about its data; it is not, by itself, proof that those claims are correct or current.
2. Connectors and retrieval
Connectors can extract descriptions from a data source or synchronize documentation, while indexing and retrieval components can find relevant material for a particular question. Those components are tools around the format, not universal OKF requirements. The format alone does not guarantee that the agent will discover or use the right concept.
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3. Agent and database runtime
The agent uses retrieved context to plan or write SQL; the database runtime executes queries under its own credentials and policies. Access controls, query validation, and execution limits must be implemented and enforced in this layer. OKF does not provide those protections.
Build a bundle around the questions your agent must answer
Start with recurring points of confusion that are not apparent from table and column names. Keep entries concise, specific, and reviewable. For example, a team might document a metric’s definition and exclusions, the meaning of a status code, or a join convention between two tables. Link related concepts where useful so a retrieval system can follow the context.
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Store the bundle with the project materials and version it so changes can be reviewed and traced. Include ownership or provenance and a review cadence appropriate to how quickly the underlying business rules change. This is an implementation pattern, not a required OKF architecture: the specification defines a representation, not a mandated directory layout, retrieval design, or release process.
Use connectors without treating them as part of the standard
The xSAVIKx/okf-skills repository documents connectors for SQLite, MySQL, PostgreSQL, and BigQuery. For those connectors, its documented command workflow includes:
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produceto create a bundle from a source. The SQL connectors document--sampleand--profileoptions for production.ingestto compare or synchronize descriptions back to a source.schemato emit a JSON description of available commands and parameters.
These are features documented by that repository, not requirements of OKF. Check the repository’s current instructions for prerequisites and compatibility before using the commands; the connector documentation is not a substitute for testing in your environment. A separate community toolkit, okf-gem, is another example of tooling and should not be confused with the normative OKF v0.2 specification.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make context available before the agent writes SQL
An implementation can retrieve relevant concepts using the user’s question, the candidate tables, or both, then pass that context to the agent alongside schema information. For a question about a metric, for example, the agent may need the metric definition and the tables that contain its inputs; for a question involving categories, it may need the documented code meanings. This describes one possible retrieval path, not a behavior guaranteed by OKF.
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Evaluate the layer on whether it retrieves the right concepts, whether those concepts cover the actual business rules, how quickly changes reach the agent, and how much effort it takes to keep documentation trustworthy. Also evaluate portability and maintenance cost. A bundle that is easy to parse but stale, incomplete, or rarely retrieved may add little practical value.
What published text-to-SQL research does—and does not—show
Research on knowledge bases and semantic context for text-to-SQL provides a reason to investigate this approach, but it is not an evaluation of OKF. Baek et al. (2025) report evaluating a method across multiple text-to-SQL datasets and database-overlap scenarios, and describe substantial gains over relevant baselines; the abstract does not provide a numeric result. See the Baek et al. paper.
Qing Ye’s 2026 preprint reports a DABStep ablation in which restoring semantic prose to a hollow data contract changed hard-task accuracy from 13.9% to 55.1%, 22.6% to 56.6%, 22.9% to 68.4%, and 37.0% to 77.4% across four model runs. The author says the gain is confined to the contract’s domain. Those figures concern that specific ablation, not OKF, and should not be read as a general accuracy guarantee. Read Qing Ye’s preprint.
Protect execution independently of documentation
Use database roles with only the permissions the agent needs, and enforce query validation and execution policy in the application or database environment. Consider controls such as restricting access to approved schemas, limiting statement types, and setting resource or result limits where appropriate. These safeguards are runtime responsibilities; a descriptive bundle cannot grant or revoke access, validate a query, or ensure that an agent follows documented conventions.
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