Analytics for the
Agentic Web

AI agents don't use browsers. They call APIs, chain tools, and coordinate with other agents. Traditional analytics is blind to this new traffic. SprTags gives you the tools to track it.

  • MCP Server endpoint for native agent tracking
  • Trace agent journeys with OpenTelemetry
  • A2A protocol support for multi-agent workflows
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MCP Protocol

track_event 12ms
track_conversion 18ms
read_analytics 45ms
get_attribution 32ms

The web is changing.
Analytics must follow.

By 2026, 40% of enterprise apps feature AI agents. These agents don't click buttons or load pages — they call APIs, use MCP tools, and delegate tasks to other agents via A2A. Your current analytics sees none of this.

Traditional Analytics

  • ✕ Requires browser JavaScript execution
  • ✕ Cookie-based identity
  • ✕ Page views and clicks as touchpoints
  • ✕ UTM parameters for attribution
  • ✕ Invisible to AI agent traffic

MCP-Powered Analytics

  • ✓ Native agent protocol — no JS needed
  • ✓ Trace ID & Agent ID identity
  • ✓ Tool calls and A2A tasks as touchpoints
  • ✓ Full agent chain attribution
  • ✓ Server-side — tracks every request

What is MCP?

The Model Context Protocol is an open standard that connects AI agents to tools, APIs, and data sources. Think of it as the USB-C for AI — one universal connector for any agent to use any tool.

Tools

Agents call MCP tools like track_event and track_conversion to fire analytics events natively — no browser required.

Resources

Agents can read your analytics data through MCP resources — ask questions like "what's my conversion rate?" and get structured answers.

Security

Each container gets its own MCP API key with scoped permissions. You control exactly which tools agents can access and at what rate.

How Agent Analytics Works

Your SGTM container becomes the analytics hub for both humans and agents.

1

Agent connects via MCP

Any AI agent (Claude, GPT, custom agents) connects to your container's MCP endpoint. No SDK integration needed — MCP is a universal protocol. 

{
  "server": "mcp://your-container.sprtags.io",
  "tools": ["track_event", "track_conversion"],
  "auth": "Bearer sk-agt-xxxx"
}
2

Agent fires events

Instead of dataLayer.push(), agents call MCP tools directly. Each call carries a trace ID linking back to the full agent reasoning chain. 

// Agent calls your MCP tool
track_event({
  event: "purchase",
  value: 99.00,
  agent_id: "shopping-assistant",
  trace_id: "abc-123-def"
})
3

SGTM routes everywhere

Your existing server-side container processes agent events exactly like human events. Forward to GA4, Meta CAPI, TikTok — or keep as first-party data. 

Agent Event
  ├─→ GA4 (Measurement Protocol)
  ├─→ Meta Conversions API
  ├─→ Your Data Warehouse
  └─→ OpenTelemetry Export

Agent-to-Agent Flow

User prompt
0ms
Shopping Agent (A2A)
120ms
Price Agent (A2A)
280ms
Your SGTM (MCP)
340ms
Checkout Agent (A2A)
520ms

Track the full agent journey with A2A

Google's Agent-to-Agent (A2A) protocol lets agents delegate tasks to each other. Each handoff creates a measurable touchpoint. With SprTags, every A2A task in the chain is tracked — giving you full attribution across multi-agent workflows.

  • Trace IDs propagated across agent chains
  • OpenTelemetry-native span export
  • Agent-level attribution for conversions
  • Task lifecycle tracking (submitted, working, completed)

Built for every agentic use case

View all use cases

Why server-side is the only option

AI agents never execute client-side JavaScript. They make direct HTTP calls. Your server-side container is the only reliable collection point for agent traffic.

0%

of agent traffic runs client-side JS

40%

of enterprise apps will have AI agents by 2026

100%

of MCP calls pass through server-side

<50ms

average MCP tool call latency

Ready to track the agentic web?

Start with a free container and enable MCP in minutes. Or talk to us about your agent analytics strategy.

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