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docs: apply GEO best practices for AI search engine optimization (#10716)
Improve documentation discoverability by AI answer engines (ChatGPT, Perplexity, Google AI) and traditional search by following Mintlify's Generative Engine Optimization guide: - Add SEO config to docs.json (indexing: all, og metatags) - Add missing title frontmatter to 24 index pages - Label 36 unlabeled code blocks with language identifiers - Fix heading hierarchy violations in 2 multitenancy pages Made-with: Cursor
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docs-mintlify/admin/ai/agent-rules.mdx

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- Domain-specific terminology
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**Example Always Rules:**
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```
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```text
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Sales efficiency is deal size divided by sales cycle length
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```
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```text
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When analyzing customer data, always consider seasonality patterns from our retail business
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```
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```text
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Revenue should be calculated using our standard GAAP accounting principles
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```
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- Scenario-specific instructions
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**Example Agent Requested Rules:**
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```text
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If you asked to analyze sales efficiency start with correlation to WSE
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```
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```text
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For customer segmentation analysis, use RFM methodology (Recency, Frequency, Monetary)
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```text
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When analyzing marketing performance, compare against industry benchmarks where available
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```
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### Domain-Specific Rules
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**E-commerce Example:**
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```text
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Always Rule: "Customer lifetime value equals average order value × purchase frequency × customer lifespan"
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Agent Requested: "For cart abandonment analysis, segment by device type and traffic source"
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```
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**SaaS Example:**
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```text
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Always Rule: "MRR growth rate should exclude one-time charges and setup fees"
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Agent Requested: "When analyzing churn, differentiate between voluntary and involuntary churn"
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Rules should provide context that agents might not inherently understand about your business:
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```text
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Always Rule: "Our peak season is Q4, with 40% of annual revenue typically occurring in December"
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Agent Requested: "For inventory analysis, consider our 6-week lead time for international suppliers"
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### Example Conflict Scenarios
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**Scenario 1: Direct Contradiction**
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```text
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Rule A (Always): "Revenue recognition follows monthly billing cycles"
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Rule B (Always): "Revenue should be recognized quarterly"
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Resolution: The agent will flag this conflict and may ask for clarification
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**Scenario 2: Complementary Rules**
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```text
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Rule A (Always): "Sales efficiency is deal size divided by sales cycle length"
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Rule B (Agent Requested): "When analyzing sales efficiency, include pipeline velocity metrics"
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Resolution: Both rules work together - B provides additional context to A
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**Scenario 3: Specificity Override**
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```text
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Rule A (Always): "Use standard deviation for all variance calculations"
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Rule B (Agent Requested): "For customer behavior analysis, use median absolute deviation instead of standard deviation"
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docs-mintlify/admin/ai/spaces-agents-models.mdx

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### Example Interaction:
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```text
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User: "Show me sales performance by region for Q4"
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Agent:
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### Space-Agent-Model Relationship:
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```text
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Space (Sales Analytics)
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├── Rules: "Revenue = quantity × price"
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├── Memories: Past Q4 analyses

docs-mintlify/admin/ai/yaml-config.mdx

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### File Structure
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```text
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your-cube-project/
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├── model/
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│ └── cubes/

docs-mintlify/admin/connect-to-data/data-sources/ms-fabric.mdx

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format. See below for tips on filling in `<SERVER_NAME>`, `<DATABASE_NAME>`,
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`<AUTH_TYPE>`, `<USER_NAME>`, and `<PASSWORD>`.
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```text
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jdbc:sqlserver://;serverName=<SERVER_NAME>.datawarehouse.pbidedicated.windows.net;database=<DATABASE_NAME>;encrypt=true;Authentication=<AUTH_TYPE>;UserName=<USER_NAME>;Password=<PASSWORD>
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```
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docs-mintlify/admin/connect-to-data/data-sources/snowflake.mdx

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In order to connect Cube to Snowflake, you need to grant certain permissions to the Snowflake role
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used by Cube. Cube requires the role to have `USAGE` on databases and schemas
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and `SELECT` on tables. An example configuration:
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```sql
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GRANT USAGE ON DATABASE ABC TO ROLE XYZ;
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GRANT USAGE ON ALL SCHEMAS IN DATABASE ABC TO ROLE XYZ;
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GRANT USAGE ON FUTURE SCHEMAS IN DATABASE ABC TO ROLE XYZ;

docs-mintlify/admin/connect-to-data/multitenancy.mdx

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</Note>
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### Multitenancy vs Multiple Data Sources
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## Multitenancy vs Multiple Data Sources
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In cases where your Cube data model is spread across multiple different data
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sources, consider using the [`data_source` cube property][ref-cube-datasource]

docs-mintlify/admin/connect-to-data/visualization-tools/index.mdx

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title: Connecting to Visualization Tools
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description: Find connection guides for BI and visualization tools that integrate with Cube.
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docs-mintlify/admin/deployment/byoc/index.mdx

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---
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title: Bring Your Own Cloud (BYOC)
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description: Deploy Cube Cloud data-plane components on your own AWS, Azure, or GCP infrastructure for full data residency control.
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docs-mintlify/admin/deployment/providers/index.mdx

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title: Cloud Providers
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description: Learn about AWS, GCP, and Azure support in Cube Cloud, including available regions and dedicated infrastructure.
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docs-mintlify/admin/deployment/vpc/aws/index.mdx

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title: AWS VPC Connectivity
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description: Establish a private network connection between Cube Cloud and your AWS VPC using PrivateLink or VPC peering.
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