ecommerce-oltp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GENIE_HOST | No | Alternate host for Genie (defaults to DATABRICKS_HOST) | |
| GENIE_TOKEN | No | Alternate token for Genie (defaults to DATABRICKS_TOKEN) | |
| DATABRICKS_HOST | Yes | Databricks workspace URL, e.g. https://your-workspace.cloud.databricks.com | |
| DATABRICKS_TOKEN | Yes | Databricks personal access token | |
| DATABRICKS_SCHEMA | No | Schema name (default: ecommerce_genie_ontology) | |
| DATABRICKS_CATALOG | No | Catalog name (default: ecommerce_genie_ontology) | ecommerce_genie_ontology |
| DATABRICKS_OLTP_SCHEMA | No | OLTP schema name | |
| DATABRICKS_WAREHOUSE_ID | No | Optional SQL warehouse ID used for queries |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_fraud_casesA | List the 15 fraud detection cases. Each case is a named SQL evidence pack. |
| list_fraud_agentsA | List the 10 Databricks Genie fraud specialists that share OLTP + dims/facts. |
| run_fraud_caseB | Run one fraud case (ids 01-15). Returns at most 50 evidence rows. |
| run_fraud_agent_casesC | Run every evidence pack owned by one of the 10 fraud specialists. |
| generate_historical_oltpC | Write customer, address, order, line, shipment, and entity_link tables. |
| generate_realtime_ordersC | Append 100-10000 new OLTP orders. year_window is latest | last_2 | last_3 | all. |
| etl_star_historicalC | Rebuild star-schema dims and fact_sales from OLTP (overwrite). |
| etl_star_cdcB | Apply Delta change feed from OLTP customer_order into fact_sales (append). |
| query_datasetA | Run a SELECT against dims, facts, or OLTP. LIMIT 50. Never dumps full tables. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Most tools have clearly distinct roles: generation, ETL, fraud case listing/execution, and querying. The main ambiguity is between run_fraud_case and run_fraud_agent_cases, but their descriptions clarify single-case versus agent-owned case batches.
All tool names follow a consistent snake_case verb_noun pattern, with recognizable prefixes like run_, list_, generate_, etl_star_, and query_. This makes the tool surface predictable and easy to navigate.
Nine tools is a well-scoped size for this server's purpose. Each tool covers a distinct phase of the workflow—data generation, ETL, fraud case evaluation, and querying—without unnecessary redundancy.
The core lifecycle of generating OLTP data, building star-schema artifacts, running fraud cases, and querying results is well covered. Minor gaps include the lack of explicit schema/reset management or direct row-level CRUD, but agents can work around these using query_dataset and generation tools.