Why use the MCP server?
- No data pipeline to build or maintain. Your assistant queries Alpharun live, so there’s nothing to sync and results are never stale.
- Rich filtering built in. Interactions can be filtered by teammate, date range, outcome, playbook criterion scores, signals, and custom fields — server-side.
- Semantic transcript search. Search calls by meaning (“customers confused about pricing”), by keyword, or both — capabilities that would be expensive to replicate on top of a raw data export.
- Analysis-ready context. The server exposes your playbooks, criteria, outcomes, signals, and custom field definitions so assistants understand your data model, not just raw records.
Setup
Follow the step-by-step setup guide in our help center: Alpharun MCP.What your assistant can do
Once connected, an assistant has access to tools for:When to use MCP vs. REST API vs. webhooks
Each integration surface is built for a different job:
A good rule of thumb: the REST API and webhooks are for systems talking to Alpharun; the MCP server is for AI assistants and agents talking to Alpharun.
If your goal is AI-powered analysis of your Alpharun data, connect through the MCP server rather than bulk-exporting interactions and transcripts via the REST API into your own datastore. A copied dataset goes stale immediately, needs its own search and filtering infrastructure, and loses the playbook context that makes the data meaningful — the MCP server gives your assistant all of that out of the box.