Claap connects to AI agents (Claude and others) through MCP, so you can ask an agent to analyze your calls in plain language. But the quality of what you get back depends almost entirely on the quality of the data you point the agent at. This is where Claap stands apart: your AI Fields turn messy call data into structured, agent-ready data.
Learn why pointing your AI agent at a structured Meeting View gives dramatically better results than raw transcripts, and how to set it up. |
Here is what that means in practice, and how to get the most out of it.
The key idea: agent-ready data
An AI agent can work from two very different kinds of input:
Raw transcripts: the full text of every call. Rich, but unstructured and huge. To analyze them, the agent has to read, sample, and score calls on the fly, which is slow, partial, and inconsistent across a large history.
A structured Meeting View with AI Fields: your calls already scored and organized into clean columns (for example a SPICED score per call, per rep). The agent reads ready-made signal instead of re-deriving it.
In other words, AI Fields do the structuring work up front, so the agent starts from analysis-ready data rather than raw material. That is what makes the output faster, more complete, and more reliable.
Before and after: a sales coaching example
We asked the same question two ways, through the same MCP connection.
Pointing the agent at the raw folder (before): The folder held 528 recordings, far too many to transcribe and score in one pass. The agent had to hand-pick a small sample of transcripts across a few reps and deals, then piece together a qualitative read. Useful, but partial, and impossible to make truly comprehensive.
Pointing the agent at the structured "SPICED Coaching" view (after): The agent loaded the view, pulled the rows across 263 scored recordings, and parsed the pre-computed SPICED scores and coaching signals systematically. The result was a clean, quantified analysis, including a per-rep breakdown (average score per SPICED dimension, per rep, across their calls), and consistent strengths and areas for improvement grounded in the whole dataset rather than a handful of examples.
Same agent, same question. The difference was entirely in the data it was given.
How to set this up
Build a Meeting View with the AI Fields you care about. In the Meetings section, open or create a view, then add the AI Fields that represent your analysis (for example your SPICED, MEDDIC, or BANT criteria). See our guide on creating views and AI Fields.
Make sure the fields are generated. Turn on Auto-run so new calls are scored automatically, and backfill your existing calls (select the recordings, then Bulk edit and Complete X recordings). A view is only agent-ready once its fields are populated.
Point your agent at the view by name. In your prompt, reference the specific view, for example: "Based on the SPICED Coaching view in Claap, analyze the strengths and areas for improvement." Naming the view tells the agent to use your structured data instead of scanning raw transcripts.
Best practices
Name your views clearly. A descriptive name like "SPICED Coaching" or "Discovery Scorecard" makes it easy to reference in a prompt and easy for the agent to find.
Keep fields populated. Auto-run plus an occasional backfill ensures the view stays complete, so the agent always analyzes your full dataset.
One view per use case. A focused view (coaching, qualification, product feedback) gives sharper results than one giant catch-all.
Reference the view explicitly. Telling the agent which view to use is the single biggest lever on output quality.
Quick answers
Do I need to prepare anything for the agent? Just a view with populated AI Fields. That is what "agent-ready" means.
What if I point it at raw recordings instead? It will still work, but it will sample and estimate rather than analyze your whole history systematically. A structured view is far more reliable.
Does this work with any AI agent? It works through Claap's MCP connection, so any MCP-compatible agent can query your views.
Want help setting up a view for your team? Reach out and we'll help you build the right AI Fields and get your data agent-ready.