Staats

Product Introduction

1. Definition: Staats is an agent-native, cookieless web analytics platform built on the Model Context Protocol (MCP). It is a technical tool designed for integration directly into AI-powered coding agents and modern IDEs, moving analytics from a passive dashboard into an active, conversational workflow.
2. Core Value Proposition: It exists to eliminate the friction of traditional analytics dashboards by enabling developers and builders to receive data-backed insights directly within their development environment. Its core proposition is dashboard-less site analytics, where the AI agent becomes an autonomous analyst that measures deploys, flags changes, and suggests next moves with contextual evidence, allowing developers to focus solely on building.

Main Features

1. Agent-Native MCP Integration: Staats connects via the open Model Context Protocol, providing a suite of tools directly to compatible coding agents like Claude Code, Cursor, and Windsurf. How it works: Developers add a single HTTP transport MCP server URL to their agent's configuration. This grants the agent persistent access to create sites, view analytics, and receive alerts, enabling a conversational interface for all data queries without leaving the IDE.
2. Autonomous, Cookieless Tracking: The platform uses a proprietary, lightweight (~1.5KB) JavaScript tracker that requires zero cookies and does not log IP addresses. How it works: It generates daily-rotating, salted visitor hashes for anonymization and uses timezone data for basic geography. This privacy-first analytics approach ensures GDPR/CPRA compliance by default and eliminates the need for cookie consent banners.
3. In-Chat Intelligence & Proactive Alerts: Analytics data is surfaced contextually within chat conversations with the coding agent. The agent can autonomously measure deploy impact by comparing pre- and post-deploy metrics, flag significant traffic anomalies or referrer spikes, and answer direct questions about visitors, top referrers, or conversion funnels without the user opening a separate dashboard.
4. Declarative & Autonomous Instrumentation: Tracking is implemented via a simple HTML data-track attribute or a window.track() function. The key innovation is that the AI agent can be instructed to add this tracking code autonomously while writing feature code, ensuring new elements are measured from their first deployment without manual developer intervention.
5. Portfolio-Wide Management: A single account and MCP connection key provides analytics for an entire portfolio of projects (from 1 to 50 sites, depending on plan). This allows developers and indie makers to compare sites side-by-side or spin up tracking for a new application directly through a chat command to their agent.

Problems Solved

1. Pain Point: Context switching and dashboard fatigue. Developers and product builders waste time interpreting charts in separate analytics dashboards, which distracts from core development work and delays data-informed decision-making.
2. Target Audience: Primary personas are Indie Hackers, Solopreneurs, and Small SaaS Development Teams who manage multiple projects and prioritize developer experience. Secondary users are Product Engineers in larger teams who need rapid, contextual insights on feature performance without complex tooling.
3. Use Cases: Essential for post-deploy validation ("Did my feature change improve conversion?"), diagnosing unexpected traffic spikes in real-time, optimizing conversion funnels by identifying drop-off points suggested by the agent, and instrumenting new features with tracking during the development phase automatically.

Unique Advantages

1. Differentiation: Unlike traditional analytics (Google Analytics) or modern product analytics (PostHog, Plausible), Staats is not a dashboard-centric tool. It shifts the interface from visual charts to a conversational, agent-driven model. Unlike other MCP tools, it is a fully-fledged, cookieless analytics engine, not just a connector to existing services.
2. Key Innovation: The fusion of a privacy-by-design, cookieless tracking infrastructure with the agent-native workflow of the Model Context Protocol. This creates a closed loop where the entity that writes the code (the AI agent) is also the primary consumer of the performance data, enabling autonomous optimization and evidence-based suggestions.

Frequently Asked Questions (FAQ)

1. How does Staats work without cookies or IP logging for web analytics? Staats uses a daily-rotating secret salt to create anonymous, ephemeral visitor hashes. Combined with timezone and user-agent data, it provides trend analytics and unique visitor counts without storing personally identifiable information (PII), ensuring compliance with privacy regulations.
2. Can I use Staats alongside Google Analytics or Plausible? Yes. The Staats tracker script is lightweight (~1.5KB) and runs independently. It can be installed concurrently with other analytics platforms without conflict, allowing for a gradual transition or comparative use.
3. What happens if my coding agent suggests a change based on Staats data? Your agent provides evidence-backed suggestions, such as identifying a leak in a checkout funnel. The developer retains full control to review the evidence (provided in chat) and approve, modify, or reject the proposed code changes, maintaining the human-in-the-loop.
4. Is Staats suitable for high-traffic SaaS applications? Yes, the Scale plan supports up to 10 million events monthly. The cookieless architecture and efficient data processing are designed for scale. However, its dashboard-less nature makes it most powerful for teams comfortable with a conversational, agent-driven data workflow.
5. How do I access raw data or export it from Staats? While the primary interface is conversational, each site includes a full CSV export function within the web dashboard (used for account management). This allows for raw data extraction for archival purposes or advanced external analysis.