Integration Proposal: SDC Agents — Deterministic Semantic Data Artifacts from Enterprise Datastores

## 🔴 Required Information

### Is your feature request related to a specific problem?

ADK developers working with enterprise data sources (SQL databases, CSV, JSON, MongoDB, BigQuery) lack tooling to produce deterministic, W3C-compliant semantic data artifacts. Current integrations focus on retrieval and generation — none produce validated schemas (XSD, SHACL, JSON-LD) or signed XML instances from structured data.

### Describe the Solution You'd Like

We maintain [SDC Agents](https://github.com/SemanticDataCharter/SDC_Agents) (`sdc-agents` on PyPI), a suite of **9 purpose-scoped ADK agents with 32 tools** that transform enterprise data into validated, multi-format semantic artifacts.

**What it does:**

- **Introspect** legacy datastores (SQL, CSV, JSON, MongoDB, BigQuery — read-only) and extract structure
- **Discover** published schemas from a catalog of 6,400+ components (FHIR, NIEM, NIH CDEs, X12, SUS, CIHI)
- **Map** source columns to semantic components with ontology links
- **Generate** XML instances from mapped data
- **Validate and sign** instances via a validation-as-a-service API
- **Distribute** artifact packages to triplestores (Fuseki, Neo4j, GraphDB), REST APIs, and filesystems
- **Assemble** new data models from component libraries

**Architecture:**
Each agent is an `LlmAgent` with a single `BaseToolset`. No agent has both datasource access and network access (security isolation by design). All tools are async, audited (JSONL), and cache-aware.

**Usage:**
```python
from sdc_agents.agents import create_introspection_agent, create_catalog_agent

# Introspect a PostgreSQL database
introspect = create_introspection_agent()

# Search the published catalog of 6,400+ semantic components
catalog = create_catalog_agent()
```

**Already supports:**
- ADK `BaseToolset` / `FunctionTool` / `LlmAgent` patterns
- MCP export via `adk_to_mcp_tool_type()`
- Docker image, CLI, PyPI package (`pip install sdc-agents`)
- 184 tests, 82% coverage

**We propose contributing:**
1. A thin wrapper module under `contributing/samples/` with usage examples
2. A documentation page for the `adk-docs` integrations directory

### Impact on your work

Enables ADK agents to produce deterministic, standards-compliant data schemas and validated instances from enterprise data sources — closing the gap between agentic AI and formal data governance. Targets healthcare, government, financial, and research domains where data provenance and schema validation are mandatory.

### Willingness to contribute

Yes — we have the integration ready. Corporate CLA for Axius SDC, Inc. is signed (2026-03-11).

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## 🟡 Recommended Information

### Additional Context

- **PyPI**: https://pypi.org/project/sdc-agents/
- **GitHub**: https://github.com/SemanticDataCharter/SDC_Agents
- **User Docs**: https://github.com/SemanticDataCharter/SDC_Agents/tree/main/docs/user
- **Standards**: W3C (XSD 1.1, RDF, OWL 2, SHACL, SPARQL), ISO 21090, ISO/IEC 21838-2 (BFO 2020)
- **Production platform**: SDCStudio (deployed on Google Cloud Run)

首先查看 contributing/samples/ 下的现有示例和 adk-docs 的 integrations 目录，然后阅读 SDC Agents 用户文档以及包的使用示例。添加一个轻量的集成包装器和一个 integrations 文档页面，并使文档中的示例与建议的 ADK 用法一致后，即可视为完成。

由索引模型根据 Issue 内容生成。