> ## Documentation Index > Fetch the complete documentation index at: https://docs.shareofmodel.ai/llms.txt > Use this file to discover all available pages before exploring further. # AI Insights: Brand Awareness Levels > Returns AI-generated insights about brand mention frequencies, explaining brand awareness levels and recall patterns. This endpoint analyzes mention rate data and generates insights like "Nike dominates with 85% mention rate, appearing in most recommendations, while smaller brands struggle below 20%, indicating significant awareness gaps." Identifies awareness leaders, visibility issues, and market penetration patterns. **When to use:** User wants interpreted awareness metrics, needs to understand recall patterns, identifies visibility gaps, or requires strategic recommendations based on mention frequencies. **Common user queries:** - "Generate insights on brand awareness" - "Explain mention rate patterns" - "Which brands have strong recall?" - "Summarize visibility gaps" - "What do the mention rates mean?" - "Show awareness insights" **Returns:** Array of insight objects interpreting mention frequencies with strategic implications. Current model in use: `gpt-5.4-mini`. Required permission: `read:analysis` ## OpenAPI ````yaml https://openapi.shareofmodel.ai/swagger.json get /v1/organizations/{organization_id}/workspaces/{workspace_id}/analyses/{analysis_id}/insights/brand_awareness/mention_rate openapi: 3.0.3 info: title: Share Of Model API version: v1 description: >- ## Model Context Protocol (MCP) In addition to this REST API, Share of Model exposes a **Model Context Protocol** server that lets AI assistants (Claude Desktop, Claude Code, MCP Inspector, custom agents…) call our endpoints directly as tools. Any MCP-compatible client can interact with Share of Model without writing custom integration code — connect once with your usual login and start asking the assistant to query the data for you. ### Connecting from Claude Desktop Open **Settings → Connectors**, scroll to the bottom and click **Add custom connector**, then paste `https://mcp.shareofmodel.ai/mcp/`. A browser window opens for you to log in with your Share of Model account (same login as the web app), and the assistant gains access to the tools. ### Connecting from Claude Code ```bash claude mcp add --transport http share-of-model https://mcp.shareofmodel.ai/mcp/ ``` The first time you call a tool, Claude Code opens your browser to complete the login. ### Connecting from MCP Inspector ```bash npx @modelcontextprotocol/inspector ``` In the Inspector UI, pick **Streamable HTTP** as transport, paste `https://mcp.shareofmodel.ai/mcp/`, and click **Connect**. The first connection prompts you to log in. ### Available tools Only endpoints tagged `mcp` in this OpenAPI spec are exposed as MCP tools, and only read-only (`GET`) routes are exposed. Everything tagged `mcp` below is callable from any compliant MCP client. ### Example prompts Once connected, try asking your assistant things like: - _"List the workspaces I have access to."_ - _"Show me the latest searches in workspace X."_ - _"Compare the share of model between brand A and brand B over the last 30 days."_ For more details on the protocol itself, see the [Model Context Protocol specification](https://modelcontextprotocol.io/). servers: - description: Production API url: https://api.shareofmodel.ai/ - description: Development API url: https://api.dev.shareofmodel.ai/ security: [] tags: - name: Auth description: Endpoints needed for API authentication. - name: Organizations description: Endpoints related to organizations, to list all available organizations. - name: Workspaces description: Endpoints related to workspaces, to list all available workspaces. - name: Analyses description: Endpoints related to analyses and analyses management. - name: Asset Evaluations description: Endpoints related to assets and asset evaluations. - name: Brand Catalog description: Endpoints related to general brand information. - name: Content Briefs description: Endpoints related to content briefs generation and optimisation. - name: Metrics description: >+ Endpoints related to brand metrics. **LEXICON** **Brand Awareness**: What opinion the LLMs have concerning specific brands, related to certain categories. **Brand Perception**: The general sentiment of the LLMs towards a brand, based on the pros and cons they mention. paths: /v1/organizations/{organization_id}/workspaces/{workspace_id}/analyses/{analysis_id}/insights/brand_awareness/mention_rate: get: tags: - Insights summary: 'AI Insights: Brand Awareness Levels' description: >- Returns AI-generated insights about brand mention frequencies, explaining brand awareness levels and recall patterns. This endpoint analyzes mention rate data and generates insights like "Nike dominates with 85% mention rate, appearing in most recommendations, while smaller brands struggle below 20%, indicating significant awareness gaps." Identifies awareness leaders, visibility issues, and market penetration patterns. **When to use:** User wants interpreted awareness metrics, needs to understand recall patterns, identifies visibility gaps, or requires strategic recommendations based on mention frequencies. **Common user queries:** - "Generate insights on brand awareness" - "Explain mention rate patterns" - "Which brands have strong recall?" - "Summarize visibility gaps" - "What do the mention rates mean?" - "Show awareness insights" **Returns:** Array of insight objects interpreting mention frequencies with strategic implications. Current model in use: `gpt-5.4-mini`. Required permission: `read:analysis` operationId: get_mention_rate_insights parameters: - in: path name: analysis_id schema: type: string format: uuid description: A UUID string identifying the analysis. required: true - in: path name: organization_id schema: type: string format: uuid description: A UUID string identifying the organization. required: true - in: path name: workspace_id schema: type: string format: uuid description: A UUID string identifying the workspace. required: true responses: '200': content: application/json: schema: type: array items: $ref: '#/components/schemas/ChartInsight' description: '' security: - Bearer: [] components: schemas: ChartInsight: type: object description: Base class with necessary methods overridden properties: insight_title: type: string insight_description: type: string required: - insight_description - insight_title securitySchemes: Bearer: type: apiKey in: header name: Authorization ````