> ## 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. # Complete Audience Segment Profiles > Returns detailed profiles for all audience segments, including demographics, beh aviors, challenges, and preferences in one comprehensive response. This endpoint provides complete segment definitions with all attributes: age, gender, location, household, cost tolerance, plus psychographic data like challenges, decision-making factors, purchase influences, and media channels. Perfect for creating personas, understanding complete customer profiles, and building targeted campaigns. Each segment represents a distinct audience cohort with unique characteristics. **When to use:** User wants complete audience profiles, needs full segment details, creates marketing personas, requires psychographic insights beyond demographics, or builds targeted campaign strategies. **Common user queries:** - "Show me all audience segments for Nike" - "What are the complete customer profiles?" - "Get detailed segment information" - "List all personas with their characteristics" - "What challenges do different segments face?" - "Show full audience breakdown" **Returns:** Array of detailed segment objects with demographics, psychographics, and Google Ads targeting data. Example: [{"name": "Young Urban Professionals", "age_range": "25-34", "gender": "all", "challenges": ["time shortage"], "media_channels": ["instagram", "youtube"]}] Required permission: `read:analysis`. ## OpenAPI ````yaml https://openapi.shareofmodel.ai/swagger.json get /v1/organizations/{organization_id}/workspaces/{workspace_id}/analyses/{analysis_id}/metrics/target_audience/segments 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/target_audience/segments: get: tags: - Metrics summary: Complete Audience Segment Profiles description: >- Returns detailed profiles for all audience segments, including demographics, beh aviors, challenges, and preferences in one comprehensive response. This endpoint provides complete segment definitions with all attributes: age, gender, location, household, cost tolerance, plus psychographic data like challenges, decision-making factors, purchase influences, and media channels. Perfect for creating personas, understanding complete customer profiles, and building targeted campaigns. Each segment represents a distinct audience cohort with unique characteristics. **When to use:** User wants complete audience profiles, needs full segment details, creates marketing personas, requires psychographic insights beyond demographics, or builds targeted campaign strategies. **Common user queries:** - "Show me all audience segments for Nike" - "What are the complete customer profiles?" - "Get detailed segment information" - "List all personas with their characteristics" - "What challenges do different segments face?" - "Show full audience breakdown" **Returns:** Array of detailed segment objects with demographics, psychographics, and Google Ads targeting data. Example: [{"name": "Young Urban Professionals", "age_range": "25-34", "gender": "all", "challenges": ["time shortage"], "media_channels": ["instagram", "youtube"]}] Required permission: `read:analysis`. operationId: target_audience_segments 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 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: 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: 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: 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: type: object properties: brand: type: string collection_date: type: string name: type: string age_range: type: string gender: type: string area_type: type: string household_type: type: string cost_tolerance: type: string challenges: type: array decision_making: type: array purchase_influences: type: array media_channels: type: array sources: type: array google_ads_segments: type: array examples: AListOfSegmentObjects.: value: - brand: Nike collection_date: '2024-10-24' name: Young Urban Professionals age_range: 25-34 gender: all area_type: urban household_type: single cost_tolerance: medium challenges: - time shortage - brand confusion - budget limit decision_making: - price - sustainability - peer opinion purchase_influences: - ads - social media - reviews media_channels: - instagram - youtube - tiktok sources: - source_1 - source_2 google_ads_segments: - category_id: '80429' segment_name: /Apparel & Accessories/Activewear display: 'YES' gmail: 'YES' discovery: 'YES' search: 'YES' shopping: 'NO' video: 'YES' generated_description: People interested in activewear summary: A list of segment objects. text/csv: schema: type: array items: type: object properties: brand: type: string collection_date: type: string name: type: string age_range: type: string gender: type: string area_type: type: string household_type: type: string cost_tolerance: type: string challenges: type: array decision_making: type: array purchase_influences: type: array media_channels: type: array sources: type: array google_ads_segments: type: array examples: AListOfSegmentObjects.: value: - brand: Nike collection_date: '2024-10-24' name: Young Urban Professionals age_range: 25-34 gender: all area_type: urban household_type: single cost_tolerance: medium challenges: - time shortage - brand confusion - budget limit decision_making: - price - sustainability - peer opinion purchase_influences: - ads - social media - reviews media_channels: - instagram - youtube - tiktok sources: - source_1 - source_2 google_ads_segments: - category_id: '80429' segment_name: /Apparel & Accessories/Activewear display: 'YES' gmail: 'YES' discovery: 'YES' search: 'YES' shopping: 'NO' video: 'YES' generated_description: People interested in activewear summary: A list of segment objects. description: '' security: - Bearer: [] components: securitySchemes: Bearer: type: apiKey in: header name: Authorization ````