> ## 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. # Google Ads Targeting Categories > Shows which Google Ads audience segments appear most frequently across the brand 's target audiences, enabling direct campaign targeting. This endpoint returns Google Ads segment IDs and names ranked by occurrence count, revealing the most relevant advertising categories for reaching the audience. Segments like "/Apparel & Accessories/Activewear" might appear in 8 out of 10 audience profiles, indicating high targeting priority. Use these IDs directly in Google Ads campaigns for precise audience reach. **When to use:** User wants to set up Google Ads campaigns, identify most relevant advertising segments, prioritize targeting categories, or understand audience overlap with Google's taxonomy. **Common user queries:** - "What Google Ads segments should I target?" - "Show me the most relevant advertising categories" - "Which audience segments appear most often?" - "Get Google Ads targeting recommendations" - "What categories match our audience?" - "List top Google Ads segments" **Returns:** Array of Google Ads segments with occurrence counts, sorted by frequency. Example: [{"google_ads_segment_id": "80429", "google_ads_segment_name": "/Apparel & Accessories/Activewear", "count": 8}] 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/google_ads_segment_occurrences 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/google_ads_segment_occurrences: get: tags: - Metrics summary: Google Ads Targeting Categories description: >- Shows which Google Ads audience segments appear most frequently across the brand 's target audiences, enabling direct campaign targeting. This endpoint returns Google Ads segment IDs and names ranked by occurrence count, revealing the most relevant advertising categories for reaching the audience. Segments like "/Apparel & Accessories/Activewear" might appear in 8 out of 10 audience profiles, indicating high targeting priority. Use these IDs directly in Google Ads campaigns for precise audience reach. **When to use:** User wants to set up Google Ads campaigns, identify most relevant advertising segments, prioritize targeting categories, or understand audience overlap with Google's taxonomy. **Common user queries:** - "What Google Ads segments should I target?" - "Show me the most relevant advertising categories" - "Which audience segments appear most often?" - "Get Google Ads targeting recommendations" - "What categories match our audience?" - "List top Google Ads segments" **Returns:** Array of Google Ads segments with occurrence counts, sorted by frequency. Example: [{"google_ads_segment_id": "80429", "google_ads_segment_name": "/Apparel & Accessories/Activewear", "count": 8}] Required permission: `read:analysis`. operationId: target_audience_google_ads_segments_occurrences 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: google_ads_segment_id: type: string google_ads_segment_name: type: string count: type: integer examples: OccurrencesOfGoogleAdsSegments: value: - google_ads_segment_id: '80428' google_ads_segment_name: /Apparel & Accessories count: 1 - google_ads_segment_id: '80429' google_ads_segment_name: /Apparel & Accessories/Activewear count: 2 - google_ads_segment_id: '80036' google_ads_segment_name: >- /Autos & Vehicles/Motor Vehicles/Motor Vehicles by Brand/Peugeot count: 1 summary: Occurrences of Google Ads Segments text/csv: schema: type: array items: type: object properties: google_ads_segment_id: type: string google_ads_segment_name: type: string count: type: integer examples: OccurrencesOfGoogleAdsSegments: value: - google_ads_segment_id: '80428' google_ads_segment_name: /Apparel & Accessories count: 1 - google_ads_segment_id: '80429' google_ads_segment_name: /Apparel & Accessories/Activewear count: 2 - google_ads_segment_id: '80036' google_ads_segment_name: >- /Autos & Vehicles/Motor Vehicles/Motor Vehicles by Brand/Peugeot count: 1 summary: Occurrences of Google Ads Segments description: '' security: - Bearer: [] components: securitySchemes: Bearer: type: apiKey in: header name: Authorization ````