> ## 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 AI Model > Shows composite performance scores broken down by AI model (ChatGPT, Gemini, Claude, etc.), revealing platform-specific brand strength variations. This endpoint calculates the composite score separately for each AI source, enabling identification of which platforms favor which brands. A brand might score 85 on ChatGPT but only 65 on Gemini, indicating inconsistent AI positioning. Critical for platform-specific optimization strategies and understanding where to focus brand building efforts across different LLM ecosystems. **When to use:** User wants to compare brand performance across AI platforms, identify model-specific weaknesses, optimize for specific LLMs, analyze consistency across sources, or find platform preferences. **Common user queries:** - "How does Nike perform on ChatGPT vs Gemini?" - "Compare brand scores across different AI models" - "Which AI favors our brand?" - "Show score variations by LLM" - "Is brand performance consistent across platforms?" - "Get composite scores by source" **Returns:** Array of brands with composite scores for each AI source. Example: [{"brand": "Nike", "source": "chatgpt-4", "composite_score": 85.5}, {"brand": "Nike", "source": "gemini", "composite_score": 72.0}] 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_source 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_source: get: tags: - Metrics summary: Brand Performance by AI Model description: >- Shows composite performance scores broken down by AI model (ChatGPT, Gemini, Claude, etc.), revealing platform-specific brand strength variations. This endpoint calculates the composite score separately for each AI source, enabling identification of which platforms favor which brands. A brand might score 85 on ChatGPT but only 65 on Gemini, indicating inconsistent AI positioning. Critical for platform-specific optimization strategies and understanding where to focus brand building efforts across different LLM ecosystems. **When to use:** User wants to compare brand performance across AI platforms, identify model-specific weaknesses, optimize for specific LLMs, analyze consistency across sources, or find platform preferences. **Common user queries:** - "How does Nike perform on ChatGPT vs Gemini?" - "Compare brand scores across different AI models" - "Which AI favors our brand?" - "Show score variations by LLM" - "Is brand performance consistent across platforms?" - "Get composite scores by source" **Returns:** Array of brands with composite scores for each AI source. Example: [{"brand": "Nike", "source": "chatgpt-4", "composite_score": 85.5}, {"brand": "Nike", "source": "gemini", "composite_score": 72.0}] Required permission: `read:analysis`. operationId: brand_composite_score_by_source 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: $ref: '#/components/schemas/BrandCompositeScoreBySource' description: '' security: - Bearer: [] components: schemas: BrandCompositeScoreBySource: type: object description: Base class with necessary methods overridden properties: brand: type: string source: type: string brand_awareness_score: type: string format: decimal pattern: ^-?\d{0,1}(?:\.\d{0,6})?$ 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 - pros_and_cons_score - source securitySchemes: Bearer: type: apiKey in: header name: Authorization ````