# Open Data Products SDK

> Agent-facing guide for the Open Data Products SDK landing page at https://opendataproducts.org/sdk/.

The Open Data Products SDK is the primary implementation toolkit for the Open Data Products standards family. It is a Python SDK and MCP server for generating, validating, explaining, searching, and traversing Open Data Product artifacts.

## Use This For

- Generating standards-aware data product artifacts with local LLMs or service providers such as Claude, OpenAI, and OpenRouter.
- Validating ODPS product specifications, ODPC catalogs, ODPG graphs, ODPV vocabulary, and data contract references.
- Creating structured context for AI agents, product owners, stewards, and engineers.
- Searching terms, products, ODPC catalog objects, data contracts, references, and graph items.
- Traversing product, objective, KPI, use-case, signal, and graph relationships.
- Exposing SDK operations to AI agents through the Model Context Protocol.

## Project Links

- SDK page: https://opendataproducts.org/sdk/
- GitHub repository: https://github.com/Open-Data-Product-Initiative/odp-agent-sdk
- Open Data Product Initiative: https://opendataproducts.org/
- Standards family:
  - ODPS: https://opendataproducts.org/v4.1/
  - ODPV: https://opendataproducts.org/odpv-v1.0/
  - ODPG: https://opendataproducts.org/odpg-v1.0/
  - ODPC: https://opendataproducts.org/odpc-v1.0/

## Install

```bash
pip install open-data-products
```

## CLI Entry Point

Use the unified command:

```bash
open-data-products
```

Common workflows shown on the SDK page:

```bash
open-data-products validate product.yaml --json
open-data-products explain product.yaml
open-data-products graph traverse catalog.yaml
open-data-products mcp serve
```

## LLM Usage

The SDK supports LLM-assisted generation workflows. Agents and developers can use local LLMs or service providers such as Claude, OpenAI, and OpenRouter to help generate standards-aware data product artifacts.

Keep generated output grounded in the standards family and validate artifacts after generation.

Recommended generation loop:

1. Load or create source context.
2. Generate or update the artifact with a local LLM or provider.
3. Validate the resulting YAML or JSON with `open-data-products validate`.
4. Explain or inspect the artifact with `open-data-products explain`.
5. Use graph traversal or search commands when relationships matter.

## MCP Usage

The SDK includes Model Context Protocol support for agent hosts.

Run:

```bash
open-data-products mcp serve
```

Use MCP when an agent needs structured resources, safe tools, validation, explanation, search, traversal, product context, standards-aware graph relationships, or LLM-assisted generation workflows.

## Standards Scope

- ODPS: data product description, access, quality, SLA, pricing, support, license, and strategy.
- ODPC: portfolio-level catalogs for products, objectives, use cases, KPIs, and demand signals.
- ODPG: graph relationships connecting products to business value.
- ODPV: shared vocabulary for humans, platforms, and AI agents.
- Data contracts: contract-aware workflows for structure, expectations, quality, access, and product handover.

## Maintainer

- Jarkko Moilanen: https://www.linkedin.com/in/jarkkomoilanen/

## Agent Notes

- Prefer `open-data-products` examples when documenting SDK workflows.
- Treat the SDK as the standards-family toolkit, not an ODPS-only tool.
- Mention both local LLMs and service providers when describing generation.
- Validate generated artifacts before presenting them as usable standards files.