MCP Server Setup

Connect Claude Desktop, Claude Code, MCP Inspector or any MCP client to Share Of Model.

In addition to the REST API, Share Of Model exposes a Model Context Protocol (MCP) server. Any MCP-compatible client — Claude Desktop, Claude Code, MCP Inspector, custom agents — can call Share Of Model endpoints directly as tools.

There is no custom integration to build: connect with your existing Share Of Model account and start querying your data in natural language.

Connect your client

Claude Desktop

1. Open the connector settings

   Open Settings → Connectors, scroll to the bottom, and click Add custom connector.

2. Paste the MCP URL

   https://mcp.shareofmodel.ai/mcp
   

   Click Connect.

3. Sign in

   A browser window opens — sign in with your Share Of Model account (same credentials as the web app). Once authenticated, the assistant has access to the available tools.

Claude Code

Run the following command:

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 authentication.

MCP Inspector

Launch the inspector:

npx @modelcontextprotocol/inspector

In the UI, select Streamable HTTP as the transport, paste the MCP URL, and click Connect:

https://mcp.shareofmodel.ai/mcp

On first connection, sign in with your Share Of Model account.

Available tools

Only endpoints tagged mcp in the OpenAPI specification are exposed as MCP tools. Both API surfaces are covered — the Core API (organisations and workspaces, analyses, brand metrics, asset evaluations) and the Search API (visibility score, presence rate, keyword gaps) — plus get_current_user, which lets the assistant tailor answers to you.

To keep interactions safe and predictable, only read-only GET endpoints are currently exposed via MCP. You can explore analyses, inspect brand-perception data, review asset evaluations, and generate strategic insights — without modifying workspace data.

The server also ships domain skills — glossaries, reporting playbooks, security and compliance notes — that the assistant loads on its own. In practice this means you can ask about a metric by name without first explaining what it measures. The documentation search tools are mounted too, so the assistant can look things up in these docs mid-conversation.

Guided workflows

Ten pre-built prompts cover the most common analyses, so you don’t have to phrase the request yourself:

Prompt	What it produces
brand_visibility_brief	Executive brief on a brand’s AI visibility.
compare_brands	Head-to-head brand comparison.
weekly_brand_update	Week-over-week metrics summary.
competitive_landscape	Top brands in a category.
persona_breakdown	Performance by target persona.
attribute_deep_dive	Deep analysis of a single attribute.
search_visibility_scorecard	Search visibility scorecard.
visibility_gap_vs_competitor	Search query gaps versus a competitor.
asset_effectiveness_report	Creative asset evaluation summary.
geo_opportunities	Cross-module GEO recommendations.

In Claude Desktop and Claude Code these appear in the prompt or slash-command picker once the connector is authenticated.

Example prompts

Once connected, ask your assistant questions like:

• List the workspaces I have access to.
• Show me all completed analyses in this workspace.
• What brands are included in the running shoes analysis?
• Compare the share of voice between Nike and Adidas over the last 30 days.
• Which brands have the strongest awareness index?
• Show me the key strengths and weaknesses for each competitor.
• How does our brand perform across ChatGPT, Gemini, and Claude?
• Give me strategic insights for this analysis.
• What assets have been evaluated and which ones scored highest?

What’s next

API Documentation Guide

Use the REST API directly.

API Reference

Browse the MCP integration spec.