> ## 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. # Brand Performance by Target Persona > Shows composite performance scores broken down by customer persona, revealing which brands resonate with different audience segments. This endpoint calculates scores separately for each persona (e.g., "young professionals", "athletes", "parents"), showing how brand performance varies across target audiences. Nike might score 90 with athletes but only 60 with seniors, indicating strong segmentation. Essential for audience-specific marketing strategies and identifying underperforming segments to target. **When to use:** User wants to see brand performance by audience segment, identify which personas favor which brands, find persona-specific strengths/weaknesses, or optimize messaging for specific demographics. **Common user queries:** - "How does Nike perform with athletes vs parents?" - "Show brand scores by persona" - "Which brands resonate with young professionals?" - "Compare performance across audience segments" - "Get persona-specific brand rankings" - "What's our score with each customer type?" **Returns:** Array of brands with composite scores for each persona. Example: [{"brand": "Nike", "persona": "Athletes", "composite_score": 92.0}, {"brand": "Nike", "persona": "Parents", "composite_score": 68.5}] Required permission: `read:analysis`. ## OpenAPI ````yaml https://openapi.shareofmodel.ai/swagger.json get /v1/organizations/{organization_id}/workspaces/{workspace_id}/analyses/{analysis_id}/metrics/summary/brand_composite_score_by_persona 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}/metrics/summary/brand_composite_score_by_persona: get: tags: - Metrics summary: Brand Performance by Target Persona description: >- Shows composite performance scores broken down by customer persona, revealing which brands resonate with different audience segments. This endpoint calculates scores separately for each persona (e.g., "young professionals", "athletes", "parents"), showing how brand performance varies across target audiences. Nike might score 90 with athletes but only 60 with seniors, indicating strong segmentation. Essential for audience-specific marketing strategies and identifying underperforming segments to target. **When to use:** User wants to see brand performance by audience segment, identify which personas favor which brands, find persona-specific strengths/weaknesses, or optimize messaging for specific demographics. **Common user queries:** - "How does Nike perform with athletes vs parents?" - "Show brand scores by persona" - "Which brands resonate with young professionals?" - "Compare performance across audience segments" - "Get persona-specific brand rankings" - "What's our score with each customer type?" **Returns:** Array of brands with composite scores for each persona. Example: [{"brand": "Nike", "persona": "Athletes", "composite_score": 92.0}, {"brand": "Nike", "persona": "Parents", "composite_score": 68.5}] Required permission: `read:analysis`. operationId: brand_composite_score_by_persona parameters: - in: path name: analysis_id schema: type: string format: uuid description: A UUID string identifying the analysis. required: true - in: query name: brand schema: type: string description: Filter by one specific brand. required: true - in: query name: collection_date__gte schema: type: string format: date description: >- Filter by a collection date being greater than or equal the specified date. YYYY-MM-DD format required: true - in: query name: collection_date__lt schema: type: string format: date description: >- Filter by a collection date being less than the specified date. YYYY-MM-DD format required: true - in: path name: organization_id schema: type: string format: uuid description: A UUID string identifying the organization. required: true - in: query name: period schema: type: string enum: - month - week description: Interval of the timeseries. - 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: $ref: '#/components/schemas/BrandCompositeScoreByPersona' description: '' security: - Bearer: [] components: schemas: BrandCompositeScoreByPersona: type: object description: Base class with necessary methods overridden properties: brand: type: string persona: type: string brand_awareness_score: type: string format: decimal pattern: ^-?\d{0,1}(?:\.\d{0,6})?$ pros_and_cons_score: type: string format: decimal pattern: ^-?\d{0,1}(?:\.\d{0,6})?$ attributes_score: type: string format: decimal pattern: ^-?\d{0,1}(?:\.\d{0,6})?$ composite_score: type: string format: decimal pattern: ^-?\d{0,1}(?:\.\d{0,6})?$ month: type: string format: date week: type: string format: date required: - attributes_score - brand - brand_awareness_score - composite_score - persona - pros_and_cons_score securitySchemes: Bearer: type: apiKey in: header name: Authorization ````