One Schema to Rule Them All: How Katzilla Normalizes 300+ API Formats Into a Single Consistent Response

The Tower of Babel Problem in Public Data

As of May 20, 2026, Katzilla aggregates over 300 free public data sources — from USGS earthquake feeds and NASA wildfire perimeters to FRED economic indicators, BLS labor statistics, Treasury debt figures, World Bank development data, WTO trade flows, Etherscan blockchain records, NWS weather alerts, and FEMA disaster declarations. Each of these agencies built their APIs independently, at different times, for different audiences. The result is a fragmented landscape that punishes any developer who tries to query more than one source at a time.

USGS returns earthquake data as GeoJSON with nested geometry.coordinates arrays. The National Weather Service wraps its alerts in CAP-formatted XML. The Bureau of Labor Statistics delivers time-series data as paginated CSV exports with non-standard column headers. FRED uses a clean JSON envelope, but its date keys differ from World Bank's. Etherscan returns hex-encoded values that require unit conversion. Individually, each format is manageable. Together, across 300+ sources, they are an integration nightmare — especially for AI agents that need to reason across domains in a single prompt cycle.

The Katzilla Normalization Layer

Katzilla solves this with a five-stage normalization pipeline that runs on every inbound response before it ever reaches your application.

Stage 1 — Format Detection: The pipeline identifies the raw format (GeoJSON, XML, CSV, JSON variants, plain text) and routes it to the appropriate parser. No configuration required on your end.

Stage 2 — Schema Mapping: Each data source has a maintained schema map that translates source-specific field names into Katzilla's universal field vocabulary. properties.mag from USGS becomes value. series_id from FRED becomes indicator_id. gasUsed from Etherscan becomes network_fee_units.

Stage 3 — Type Coercion: All numeric strings become numbers. All dates are normalized to ISO 8601 UTC. Boolean-like strings ("Y", "1", "true") become actual booleans. Hex values are decoded and annotated with their unit.

Stage 4 — Quality Metadata: Every Katzilla response includes a _meta block that tells you how fresh the data is, what the upstream source's stated update frequency is, whether the record was retrieved from cache or live, and a confidence score derived from the source's historical uptime and data completeness.

Stage 5 — Citation Injection: Because AI agents need traceable outputs, every response carries a _citation object with the upstream URL, the source agency name, the data license, and the exact timestamp of the upstream record. Your agent's outputs are auditable by default.

What a Normalized Response Looks Like

Here is a real query using the Katzilla SDK today, May 20, 2026, pulling a USGS earthquake event and a FEMA disaster declaration in a single chained call:

import { Katzilla } from '@katzilla/sdk';

const kz = new Katzilla({ apiKey: process.env.KATZILLA_API_KEY });

// Fetch latest M4.0+ earthquake and active FEMA disasters in parallel
const [quake, disaster] = await Promise.all([
  kz.query('usgs.earthquakes', {
    minMagnitude: 4.0,
    limit: 1,
    sort: 'time'
  }),
  kz.query('fema.disasters', {
    status: 'active',
    limit: 1
  })
]);

// Both responses share the same envelope — no custom parsing needed
console.log(quake.data[0].value);          // 4.3 (magnitude, already a number)
console.log(quake.data[0].location.lat);   // 37.621
console.log(quake._meta.freshness_seconds); // 41
console.log(quake._citation.source_url);    // https://earthquake.usgs.gov/...

console.log(disaster.data[0].indicator_id); // 'DR-4823'
console.log(disaster.data[0].timestamp);    // '2026-05-19T14:32:00Z'
console.log(disaster._citation.license);    // 'Public Domain (USA.gov)'

The shape of quake and disaster is identical at the envelope level. Your agent iterates, filters, and reasons across both without a single format-specific branch in your code.

Why This Matters for AI Agents

Large language models and autonomous agents are increasingly expected to synthesize real-world data across multiple domains simultaneously — correlating labor market shifts from BLS with Treasury debt movements, or overlaying NASA wildfire perimeters with NWS red-flag alerts. That kind of cross-domain reasoning is only practical when the underlying data arrives in a consistent, trustworthy shape.

Katzilla's normalization layer means your agent spends zero tokens parsing formats and 100% of its context window reasoning about what the data means. On May 20, 2026, that is the difference between an agent that answers questions and one that actually understands the world.