Backengine
About
The customer-context layer for your revenue team — available to Claude and any MCP client. BackEngine is a multi-tenant SaaS platform that ingests all of an organization's customer and prospect communications — Slack thr
Details
- Author
- BackEngine-ai
- GitHub stars
- 1
- Downloads
- 323
- Categories
- AI, Communication
Jump to
- Website: https://backengine.ai
- MCP endpoint: https://backengine-prod.backengine.ai/mcp
- Model Context Protocol: https://modelcontextprotocol.io
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
BackengineCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
The README includes setup instructions such as "command": "npx",.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"backengine": {
"backengine": {
"type": "streamable-http",
"url": "https://backengine-prod.backengine.ai/mcp"
}
}
}
}McpServers
{
"backengine": {
"type": "streamable-http",
"url": "https://backengine-prod.backengine.ai/mcp"
}
}The customer-context layer for your revenue team — available to Claude and any MCP client. BackEngine is a multi-tenant SaaS platform that ingests all of an organization's customer and prospect communications — Slack threads, emails, call and meeting transcripts, and support tickets — and distills them into structured, queryable context. Raw conversations are processed into signals (categorized, attributed moments), sources (the underlying transcripts, emails, and tickets), and rolling project overviews, all isolated per tenant and scoped by role and access controls. The MCP server exposes this layer over the Model Context Protocol, so an LLM client can query customer signals, reconstruct context, prep for meetings, surface at-risk accounts, and draft grounded outreach from real conversation history — without leaving the chat.
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