> ## 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.

# Create analysis clusters

> Run clustering process for a given analysis. Required permission: `write:analysis`.



## OpenAPI

````yaml https://openapi.shareofmodel.ai/swagger.json post /v1/organizations/{organization_id}/workspaces/{workspace_id}/analyses/{id}/create_clusters
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/{id}/create_clusters:
    post:
      tags:
        - Analyses
      summary: Create analysis clusters
      description: >-
        Run clustering process for a given analysis. Required permission:
        `write:analysis`.
      operationId: create_clusters
      parameters:
        - in: query
          name: cluster_group
          schema:
            type: string
            enum:
              - all_cons
              - all_pros
          description: A string representing the group of clusters to create.
        - in: query
          name: cluster_number
          schema:
            type: integer
            minimum: 5
            maximum: 8
          description: >-
            A number between 5 and 8, both included, representing the number of
            clusters to create.
        - in: path
          name: id
          schema:
            type: string
            format: uuid
          description: A UUID string identifying this analysis.
          required: true
        - in: path
          name: organization_id
          schema:
            type: string
            format: uuid
          description: A UUID string identifying the organization.
          required: true
        - in: path
          name: workspace_id
          schema:
            type: string
            format: uuid
          description: A UUID string identifying the workspace.
          required: true
      responses:
        '200':
          description: Pros and cons clustering creation successfully launched.
      security:
        - Bearer: []
components:
  securitySchemes:
    Bearer:
      type: apiKey
      in: header
      name: Authorization

````