databricks-unity-gateway

Name: databricks-unity-gateway
Author: databricks

github.com/databricksOther37341 installsUpdated 1d ago

About databricks-unity-gateway

databricks-unity-gateway is a skill for managing Unity Gateway services through the Databricks CLI. It covers model, MCP, and model provider services, with guidance for lifecycle operations, access controls, and querying.

Key features

01Manage model, MCP, and provider services
02Create, inspect, update, and delete services
03Use CLI configuration and authentication profiles
04Manage access permissions and spending controls
05Query model and model provider services

Use cases

01Register or update an MCP service
02Manage Unity Gateway model service resources
03Review or change service access permissions
04Query a model or model provider service

MCP support

The skill manages Unity Gateway MCP services through `databricks ai-gateway`, including their creation, retrieval, listing, updates, and deletion. It also directs users to guidance for registering an external MCP server.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/databricks/databricks-agent-skills --skill databricks-unity-gateway

After installing, type/databricks-unity-gatewayin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

agents/

openai.yaml

assets/

databricks.svg

references/

account-access.md

budgets.md

mcp-services.md

model-provider-services.md

model-services.md

model-serving-migration.md

permissions.md

querying.md

Details

From the project README

Unity Gateway

Use the parent databricks-core skill for CLI authentication and profile selection.

Unity Gateway provides Unity Catalog securables for governed access to AI services. The CLI command group keeps the earlier product name: databricks ai-gateway --help.

Choose the Correct Gateway

Request	Use
Unity Gateway model service, MCP service, or model provider service	This skill and `databricks ai-gateway`
AI Gateway configuration on a Model Serving endpoint, including `put-ai-gateway`	`databricks-model-serving` and `databricks serving-endpoints`
Lakeflow Connect ingestion gateway	`databricks-lakeflow-connect`

Treat Unity AI Gateway as an earlier name for Unity Gateway when the resource is a Unity Catalog securable. Do not translate legacy Model Serving AI Gateway resources into Unity Gateway resources unless the user explicitly requests a migration.

CLI Workflow

Use the Databricks CLI for resource lifecycle and permission management. Do not substitute direct REST calls or a Databricks SDK for those operations. Python clients are supported for querying model and model provider services; read Query services.

An explicit request for Unity Gateway, Unity AI Gateway, or databricks ai-gateway belongs to this skill. Do not switch to databricks-model-serving merely because the request involves a model service or a workspace. Use that skill only for a legacy per-endpoint AI Gateway operation or when a provisioned-throughput destination requires Model Serving endpoint details.

Verify the CLI meets the minimum version:
```
databricks --version
```
Inspect the command and the relevant operation before constructing a payload:
```
databricks ai-gateway --help
databricks ai-gateway <operation> --help
```
Pass create and update configuration through --json. Use an explicit profile when profile-based authentication is required:
```
databricks ai-gateway <operation> --json @payload.json --profile <PROFILE>
```

Do not invent JSON fields from similarly named legacy APIs. Build the payload from the installed CLI help and the Unity Gateway documentation for the selected service type.

Service Types

Service type	CLI operations
Model service	`create-model-service`, `get-model-service`, `list-model-services`, `update-model-service`, `delete-model-service`; read Model services
MCP service	`create-mcp-service`, `get-mcp-service`, `list-mcp-services`, `update-mcp-service`, `delete-mcp-service`; read MCP services
Model provider service	`create-model-provider-service`, `get-model-provider-service`, `list-model-provider-services`, `update-model-provider-service`, `delete-model-provider-service`; read Model provider services

Querying and permissions

Before writing Python that invokes a model or model provider service, read references/querying.md.
Before checking, granting, or revoking Unity Gateway access, read references/permissions.md.
Before any model-service operation, read references/model-services.md. It contains the current resource-name conventions, JSON payload fields, required-input gate, and lifecycle commands.
Before any MCP-service operation, read references/mcp-services.md for the equivalent MCP-specific contract.
Before any model-provider-service operation, read references/model-provider-services.md.
Before setting up a spend budget or spend alert on Unity Gateway usage, read references/budgets.md.
Before any account-scoped operation (account groups as grant principals, account budgets), read references/account-access.md.

Do not fetch public documentation for fields already covered by these references. Consult the authoritative documentation only when a required field is absent from the reference or the user explicitly asks for the latest documentation. Do not assign a Beta or preview status unless the installed CLI help or current documentation explicitly does so.

Migration from Model Serving to Unity Gateway

When migrating from workspace-scoped Model Serving endpoints to Unity Catalog-scoped Unity Gateway services, read references/model-serving-migration.md.

Authoritative Documentation

Comments

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Details

Author: databricks
Repository: databricks/databricks-agent-skills
Category: Other
Installs: 41
GitHub stars: 373

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