What agents need from your analytics API
AI agents require structured JSON-RPC tool endpoints with strict parameter typing, sub-15ms latency, and zero HTML scraping.
Autonomous AI agents making operational decisions cannot parse messy HTML dashboards or bloated 500-field JSON payloads. When agents are forced to scrape web UIs or hallucinate over uncompressed logs, operational error rates skyrocket.
The emerging standard for AI tool execution is Anthropic's Model Context Protocol (MCP). MCP provides clean, deterministic JSON-RPC specifications for analytical queries.
Tracabit exposes native MCP tool endpoints that allow LLM agents (Claude, Cursor, bespoke bots) to inspect real-time bounce rates, conversion funnels, and error clusters in under 15ms without intermediate UI layers.
{
"name": "tracabit.get_funnel_summary",
"description": "Returns multi-step conversion and drop-off rates",
"parameters": {
"type": "object",
"properties": {
"project_id": { "type": "string" },
"period": { "type": "string", "enum": ["24h", "7d", "30d"] }
},
"required": ["project_id"]
}
}Tracabit records traffic, events, crashes and web vitals with one script tag, and lets your coding agent read them over MCP.
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