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

# Overview

> Structured trading intelligence from high-signal public trader activity.

Centaur turns high-signal trader activity into a structured, source-backed data layer for market workflows.

Today, Centaur monitors curated Telegram sources and is rolling out X-backed source-message coverage where supported by the public read surface. The product vision is broader: one intelligence layer across the platforms where meaningful market signal is generated. The docs separate current coverage from that roadmap so product, API, and MCP behavior stay explicit.

## The system

Centaur ingests monitored trader communications, classifies messages that contain trade activity, and extracts structured records from them. The output is a queryable data layer for:

* source messages
* trade events
* positions
* open-position state
* trader and asset discovery
* time-based performance stats
* generated narrative summaries

## The data

* **Structured trade events**: opens, increases, decreases, and closes.
* **Current positioning**: open long/short exposure by trader or asset.
* **Track records**: time-based performance, win rate, asset focus, and related metrics.
* **Source transparency**: source-message references, previews, and original-source links when available.
* **Generated narrative summaries**: compact market narrative summaries for source windows and aggregate windows.

## Two ways to use Centaur

**Product app**: discovery, profiles, Feed, Following, Assistant, alerts, API keys, and connected apps.

**API & MCP**: read-only REST and agent-native MCP surfaces for integrations, analytics, and agent workflows.

Start with [Product Surfaces](/docs/platform/product-surfaces) for the app or [Quickstart](/docs/guides/quickstart) for API and MCP access.