> ## 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 Ratings on Specific Attributes

> Shows how AI models rate each brand on specific attributes like price, quality,
                   comfort, durability, etc., revealing brand strengths and weaknesses.

This endpoint returns AI-generated ratings (typically 0-10 scale)
for each brand on configured attributes. Nike might score 9/10 on "style" but 4/10 on "price", revealing perception gaps. Perfect for understanding competitive positioning, identifying brand strengths to emphasize, and weaknesses to address in messaging.

**When to use:** User wants to compare brands on specific attributes, identify strengths and weaknesses, understand perception on dimensions like price/quality/comfort, create positioning matrices, or find competitive advantages.

**Common user queries:**
- "How is Nike rated on comfort?"
- "Compare brand ratings on price"
- "What are each brand's strengths and weaknesses?"
- "Show attribute scores for all brands"
- "Which brand scores highest on quality?"
- "Get ratings breakdown by attribute"

**Returns:** Array with brand, attribute, and average rating scores.
Example: [{"brand": "Nike", "attribute": "comfort", "average_rating": "8.00"}, {"brand": "Nike",
"attribute": "price", "average_rating": "7.50"}]

Required permission: `read:analysis`.



## OpenAPI

````yaml https://openapi.shareofmodel.ai/swagger.json get /v1/organizations/{organization_id}/workspaces/{workspace_id}/analyses/{analysis_id}/metrics/brand_guided_sentiments/average_attribute_rating
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/brand_guided_sentiments/average_attribute_rating:
    get:
      tags:
        - Metrics
      summary: Brand Ratings on Specific Attributes
      description: >-
        Shows how AI models rate each brand on specific attributes like price,
        quality,
                           comfort, durability, etc., revealing brand strengths and weaknesses.

        This endpoint returns AI-generated ratings (typically 0-10 scale)

        for each brand on configured attributes. Nike might score 9/10 on
        "style" but 4/10 on "price", revealing perception gaps. Perfect for
        understanding competitive positioning, identifying brand strengths to
        emphasize, and weaknesses to address in messaging.


        **When to use:** User wants to compare brands on specific attributes,
        identify strengths and weaknesses, understand perception on dimensions
        like price/quality/comfort, create positioning matrices, or find
        competitive advantages.


        **Common user queries:**

        - "How is Nike rated on comfort?"

        - "Compare brand ratings on price"

        - "What are each brand's strengths and weaknesses?"

        - "Show attribute scores for all brands"

        - "Which brand scores highest on quality?"

        - "Get ratings breakdown by attribute"


        **Returns:** Array with brand, attribute, and average rating scores.

        Example: [{"brand": "Nike", "attribute": "comfort", "average_rating":
        "8.00"}, {"brand": "Nike",

        "attribute": "price", "average_rating": "7.50"}]


        Required permission: `read:analysis`.
      operationId: brand_guided_sentiment_average_attribute_rating
      parameters:
        - in: path
          name: analysis_id
          schema:
            type: string
            format: uuid
          description: A UUID string identifying the analysis.
          required: true
        - in: query
          name: attribute
          schema:
            type: string
          description: Filter by one specific attribute.
        - in: query
          name: attribute__in
          schema:
            type: string
          description: >-
            Filter by a list of attributes. Values should be comma separated,
            without brackets or spaces
        - in: query
          name: brand
          schema:
            type: string
          description: Filter by one specific brand.
        - in: query
          name: brand__in
          schema:
            type: string
          description: >-
            Filter by a list of brands. Values should be comma separated,
            without brackets or spaces
        - in: query
          name: collection_date
          schema:
            type: string
            format: date
          description: >-
            Filter by a collection date being equal to the specified date.
            YYYY-MM-DD format
        - in: query
          name: collection_date__gt
          schema:
            type: string
            format: date
          description: >-
            Filter by a collection date being greater than the specified date.
            YYYY-MM-DD format
        - 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
        - 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
        - in: query
          name: collection_date__lte
          schema:
            type: string
            format: date
          description: >-
            Filter by a collection date being less than or equal the specified
            date. YYYY-MM-DD format
        - in: query
          name: country
          schema:
            type: string
          description: Filter by one specific country.
        - in: query
          name: country__in
          schema:
            type: string
          description: >-
            Filter by a list of countries. Values should be comma separated,
            without brackets or spaces
        - in: query
          name: format
          schema:
            type: string
            enum:
              - csv
              - json
        - in: path
          name: organization_id
          schema:
            type: string
            format: uuid
          description: A UUID string identifying the organization.
          required: true
        - in: query
          name: persona
          schema:
            type: string
          description: Filter by one specific persona.
        - in: query
          name: persona__in
          schema:
            type: string
          description: >-
            Filter by a list of personas. Values should be comma separated,
            without brackets or spaces
        - in: query
          name: source
          schema:
            type: string
            enum:
              - claude-3-5-sonnet
              - claude-4-5-sonnet
              - claude-4-6-sonnet
              - claude-4-sonnet
              - deepseek-chat
              - deepseek-reasoner
              - deepseek-v3.2-maas
              - gemini-1.5-pro
              - gemini-2.0-flash
              - gemini-2.5-flash
              - gemini-2.5-flash-grounded
              - gemini-2.5-flash-lite
              - gemini-3-flash-preview
              - google-ai-mode
              - gpt-3.5-turbo
              - gpt-4-turbo
              - gpt-4o
              - gpt-4o-mini-search
              - gpt-5
              - gpt-5.2
              - gpt-5.4-mini
              - meta-llama-3.1-70B-instruct-turbo
              - meta-llama-3.2-70B-instruct-turbo
              - meta-llama-3.3-70B-instruct-turbo
              - meta-llama-4-maverick
              - mistral-7B-instruct
              - perplexity-sonar
              - rufus
          description: Filter by one specific source.
        - in: query
          name: source__in
          schema:
            type: string
            enum:
              - claude-3-5-sonnet
              - claude-4-5-sonnet
              - claude-4-6-sonnet
              - claude-4-sonnet
              - deepseek-chat
              - deepseek-reasoner
              - deepseek-v3.2-maas
              - gemini-1.5-pro
              - gemini-2.0-flash
              - gemini-2.5-flash
              - gemini-2.5-flash-grounded
              - gemini-2.5-flash-lite
              - gemini-3-flash-preview
              - google-ai-mode
              - gpt-3.5-turbo
              - gpt-4-turbo
              - gpt-4o
              - gpt-4o-mini-search
              - gpt-5
              - gpt-5.2
              - gpt-5.4-mini
              - meta-llama-3.1-70B-instruct-turbo
              - meta-llama-3.2-70B-instruct-turbo
              - meta-llama-3.3-70B-instruct-turbo
              - meta-llama-4-maverick
              - mistral-7B-instruct
              - perplexity-sonar
              - rufus
          description: >-
            Filter by a list of sources. Values should be comma separated,
            without brackets or spaces
        - in: query
          name: translate_to_en
          schema:
            type: boolean
          description: If true, translates attributes to English. Defaults to false.
        - 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/BrandGuidedSentimentAverageAttributeRating
              examples:
                ResponseExample:
                  value:
                    - - brand: Salomon
                        attribute: comfort
                        average_rating: '5.33'
                      - brand: Hoka
                        attribute: comfort
                        average_rating: '5.67'
                      - brand: Adidas
                        attribute: comfort
                        average_rating: '7.00'
                      - brand: Hoka
                        attribute: price
                        average_rating: '7.00'
                      - brand: Nike
                        attribute: price
                        average_rating: '7.50'
                      - brand: Adidas
                        attribute: price
                        average_rating: '8.00'
                      - brand: Nike
                        attribute: comfort
                        average_rating: '8.00'
                  summary: Response example
            text/csv:
              schema:
                type: array
                items:
                  $ref: >-
                    #/components/schemas/BrandGuidedSentimentAverageAttributeRating
              examples:
                ResponseExample:
                  value:
                    - - brand: Salomon
                        attribute: comfort
                        average_rating: '5.33'
                      - brand: Hoka
                        attribute: comfort
                        average_rating: '5.67'
                      - brand: Adidas
                        attribute: comfort
                        average_rating: '7.00'
                      - brand: Hoka
                        attribute: price
                        average_rating: '7.00'
                      - brand: Nike
                        attribute: price
                        average_rating: '7.50'
                      - brand: Adidas
                        attribute: price
                        average_rating: '8.00'
                      - brand: Nike
                        attribute: comfort
                        average_rating: '8.00'
                  summary: Response example
          description: ''
      security:
        - Bearer: []
components:
  schemas:
    BrandGuidedSentimentAverageAttributeRating:
      type: object
      description: Base class with necessary methods overridden
      properties:
        brand:
          type: string
        attribute:
          type: string
        average_rating:
          type: string
          format: decimal
          pattern: ^-?\d{0,2}(?:\.\d{0,2})?$
      required:
        - attribute
        - average_rating
        - brand
  securitySchemes:
    Bearer:
      type: apiKey
      in: header
      name: Authorization

````