> ## Documentation Index
> Fetch the complete documentation index at: https://docs.poly.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Query your data

> An end-to-end walkthrough of exploring conversation data with Data MCP — from discovering metrics to reading a single transcript.

Data MCP turns conversation data into something you can interrogate in plain language. The flow below goes from "what can I even filter on?" to a single transcript, using the [tools](/mcp/data/capabilities) in the order you'd naturally reach for them.

<Note>
  You don't call these tools by hand — describe what you want and let the client chain the tool calls. Example prompts are shown throughout. Every tool takes an optional `project_id`; scope your questions to a project when your account spans several.
</Note>

## 1. Discover what you can filter on

Start with [`get-metrics`](/mcp/data/capabilities#get-metrics) to see the metrics available for your account — the built-in ones plus any custom metrics your projects define. This tells you what you can filter and sort conversations by.

> "What metrics can I filter conversations on for project `acme-support`?"

## 2. Find the conversations you care about

Use [`search-conversations`](/mcp/data/capabilities#search-conversations) to filter on those metrics over a date range, then sort and page through the results. Combine filters with `and`/`or` and sort by start time, duration, or ID.

> "Find last week's conversations with a poly\_score below 3, longest first."

## 3. Search across transcripts

When you're chasing a phrase rather than a metric, [`search-transcripts`](/mcp/data/capabilities#search-transcripts) does full-text search over transcript turns and returns each match with surrounding turns for context.

> "Search transcripts for 'cancel my policy' in the last 30 days and show a couple of turns either side."

## 4. Read a full conversation

Once you've found a conversation worth reading, [`get-conversation`](/mcp/data/capabilities#get-conversation) returns every turn, top to bottom.

> "Show me the full transcript for conversation `abc-123`."

## Why this matters

Each tool is small, but together they cover the loop analysts actually run: *what can I measure → which conversations fit → where does the phrase show up → what happened in this one.* Because it all runs through your MCP client, you can follow a thread — from an aggregate concern to a single call — without leaving your chat.

<Tip>
  Keep results tight — filter to the conversations that matter and scope to a `project_id` — so the model only ingests what the task needs. See [Security & safe use](/mcp/data/security).
</Tip>