LLM Brand Monitor MCP Server
About
MCP server for LLM Brand Monitor — track how AI models mention your brand. Requires an API key (LBM_API_KEY) from llmbrandmonitor.com.
Details
- Author
- serpstatglobal
- Categories
- Other, Marketing
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Setup
Install LLM Brand Monitor MCP Server in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/serpstatglobal/llm-brand-monitor-mcp
Follow the installation instructions in the repository README, then restart your MCP client.
MCP server for LLM Brand Monitor — track how AI models mention your brand. Requires an API key (LBM_API_KEY) from llmbrandmonitor.com.
MCPserver forLLM Brand Monitor— a platform that tracks how AI models mention your brand.
Connect Claude, Cursor, Windsurf, or any MCP-compatible client to manage brand monitoring projects, run scans across 350+ LLMs, and analyze results — all through natural language.
Add to yourclaude_desktop_config.json:
{ "mcpServers": { "lbm": { "command": "npx", "args": ["-y", "@serpstat/llm-brand-monitor-mcp"], "env": { "LBM_API_KEY": "lbm_your_key_here" } } } }
claude mcp add lbm-mcp -e LBM_API_KEY=lbm_your_key_here -- npx -y @serpstat/llm-brand-monitor-mcp
git clone https://github.com/SerpstatGlobal/llm-brand-monitor-mcp.git cd llm-brand-monitor-mcp npm install && npm run build
{ "mcpServers": { "lbm": { "command": "node", "args": ["/path/to/llm-brand-monitor-mcp/dist/index.js"], "env": { "LBM_API_KEY": "lbm_your_key_here" } } } }
LBM_API_KEY=lbm_your_key_here npx @modelcontextprotocol/inspector node dist/index.js
- Sign up atllmbrandmonitor.com
- Open yourProfile page
- Copy your API key (starts withlbm_)
You: "What brand monitoring projects do I have?" Claude: → lbm_list_projects You: "Run a scan on the Serpstat project" Claude: → lbm_run_scan (asks you to confirm — scans spend credits) → lbm_get_scan_status (polls until complete) You: "Show me the results — which models mentioned my brand?" Claude: → lbm_list_results You: "What did GPT-5 say exactly?" Claude: → lbm_get_transcript You: "Who are my competitors according to AI models?" Claude: → lbm_list_competitors
All list tools returncompact CSV by defaultinstead of verbose JSON. This reduces token usage by 80–96%, keeping responses within context limits.
# Default (CSV) — 3-6 key columns competitor,mentions,visibility_pct Competitor A,178,72.4 Competitor B,105,42.7 # Full JSON — pass include_all_fields: true {"data": [{"competitor_id": "...", "competitor_name": "Competitor A", ...}]}
All list tools supportoffsetandlimitfor pagination.
Errors include actionable hints for the LLM:
npm install npm run build # TypeScript → dist/ npm test # 126 tests
Full REST API docs:llmbrandmonitor.com/api-docs
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