Scoring Model
How Colter scores agent readiness across 5 dimensions. Weights, signals, score ranges, and how to improve.
TL;DR: Colter scores stores from 0 to 100 across transaction, discovery, ecosystem, content quality, and security. The fastest way to improve the score is usually UCP, ACP, JSON-LD,
llms.txt, and security headers.
Formula
composite = round(
transaction * 0.30 +
discovery * 0.25 +
ecosystem * 0.20 +
content_quality * 0.15 +
security * 0.10
)
Dimensions
| Dimension | Weight | High-value signals |
|---|---|---|
| Transaction | 30% | UCP, ACP, checkout capabilities, fast protocol endpoints, compatible PSP |
| Discovery | 25% | JSON-LD, sitemap, llms.txt, ai-plugin.json, robots posture, Open Graph |
| Ecosystem | 20% | Google coverage, OpenAI coverage, MCP, WebMCP |
| Content Quality | 15% | Complete product schema, price, availability, images, SSR |
| Security | 10% | HTTPS, HSTS, CSP, XFO, X-Content-Type-Options, rate limiting |
Score Bands
| Score | Meaning |
|---|---|
75-100 | Strong readiness |
60-74 | Basic readiness |
40-59 | Needs work |
0-39 | Not ready |
Agentic Commerce Level
Colter also emits an agentic_level and label:
| Level | Label | Meaning |
|---|---|---|
| 1 | Web Forms | Basic site presence |
| 2 | Descriptive Search | Agents can find and read the catalog |
| 3 | Persistence | Stronger agent identity and handoff support |
| 4 | Delegation | Agents can browse, cart, and checkout |
| 4.9 max | Progress toward Level 5 | Level 5 behavior is not directly measurable today |
Highest-Impact Fixes
| Fix | Typical effect |
|---|---|
| Add UCP | Big transaction and ecosystem lift |
| Add ACP | Big transaction and ecosystem lift |
| Add complete Product JSON-LD | Discovery and content quality lift |
Add llms.txt | Discovery and ecosystem lift |
| Add security headers | Security lift |
How To Use This Page
- Use Check to see current scores.
- Use Fix to get ranked improvements.
- Use Verify when you need conformance evidence.