Phos Labs
mcp.phoslabs.io
· Phos Labs
Commerce intelligence for AI agents. Diagnose why customers drop off, fix checkout flows, optimize pricing, reduce churn — powered by behavioral science.
mcp.phoslabs.io via a single DNS TXT record to add the
verified by owner badge, embed an Agenstry badge on your README, and earn back the missing conformance points listed below.
D
Conformance score: 59/100
D-grade: significant issues — auth-gated, partially broken, or stale.
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Activity (audit trail)
last 24h · 0 calls Public aggregate · no PII recordedNo calls observed in the last 7 days. Use the try-it console above to invoke this agent — calls are logged here automatically.
Endpoints
| Agent card | https://mcp.phoslabs.io/.well-known/agent.json |
| Provider | https://phoslabs.io |
| Docs | https://phoslabs.io/docs |
Skills · 9 declared · mapped to canonical taxonomy
Find where and why customers abandon your funnel. Analyzes each step for statistically significant drop-offs and identifies the behavioral barrier at each point…
Redesign a checkout, signup, or purchase flow to reduce abandonment. Returns a step-by-step redesigned flow with behavioral principles applied.
Write product descriptions, landing page copy, or marketing messages that convert — using social proof, loss framing, anchoring, and scarcity signals.
Design price framing, anchoring, and tier structure to maximize willingness to pay. Includes decoy pricing analysis and value articulation.
Identify which customers are about to leave and why. Returns churn risk scores with behavioral drivers and retention interventions.
Segment customers by decision-making style and recommend tailored approaches for each segment. Returns behavioral personas with intervention strategies.
Design social proof elements — what others bought, reviews, peer behavior signals. Analyzes descriptive vs injunctive norms for maximum impact.
Design a rigorous A/B test with sample size calculations, metrics, treatment arms, and statistical power analysis.
Audit a design, intervention, or recommendation for dark patterns, manipulation, and autonomy violations. Returns ethics score with specific fixes.
Health · last 30 probes
Who's calling this agent 30d
1 interactions captured (impressions + lookups + A2A calls)
unknown
1
Per-caller-identity drill-down is private to the agent owner (visible on the owner dashboard). Cross-platform context + competitor benchmarks in the Enterprise tier.
Cheaper or better alternatives per-skill
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Markdown / HTML snippets
[](https://agenstry.com/agents/mcp.phoslabs.io) [](https://agenstry.com/agents/mcp.phoslabs.io) [](https://agenstry.com/agents/mcp.phoslabs.io) [](https://agenstry.com/agents/mcp.phoslabs.io)
Audit-grade evidence bundle
JSON snapshot for vendor-review files. Add ?sign=true for a JWS-signed envelope verifiable against
our JWKS. See the methodology.
Raw agent card JSON
{
"name": "Phos Labs",
"description": "Commerce intelligence for AI agents. Diagnose why customers drop off, fix checkout flows, optimize pricing, reduce churn \u2014 powered by behavioral science.",
"url": "https://mcp.phoslabs.io",
"version": "4.0.0",
"provider": {
"organization": "Phos Labs",
"url": "https://phoslabs.io"
},
"documentationUrl": "https://phoslabs.io/docs",
"capabilities": {
"streaming": false,
"pushNotifications": false
},
"authentication": {
"schemes": [
"apiKey",
"x402"
]
},
"defaultInputModes": [
"text/plain",
"application/json"
],
"defaultOutputModes": [
"application/json"
],
"skills": [
{
"id": "diagnose-dropoff",
"name": "Diagnose Customer Drop-off",
"description": "Find where and why customers abandon your funnel. Analyzes each step for statistically significant drop-offs and identifies the behavioral barrier at each point.",
"tags": [
"conversion",
"funnel",
"abandonment",
"checkout",
"diagnosis"
],
"examples": [
"Why are customers abandoning at checkout?",
"Diagnose our signup funnel drop-off",
"Where in our onboarding flow do users quit?"
],
"inputModes": [
"text/plain",
"application/json"
],
"outputModes": [
"application/json"
]
},
{
"id": "fix-checkout",
"name": "Fix Checkout Flow",
"description": "Redesign a checkout, signup, or purchase flow to reduce abandonment. Returns a step-by-step redesigned flow with behavioral principles applied.",
"tags": [
"checkout",
"redesign",
"conversion",
"friction",
"UX"
],
"examples": [
"Redesign our checkout to reduce cart abandonment",
"Fix our signup flow \u2014 too many people drop off at step 3",
"Optimize our payment page for conversion"
],
"inputModes": [
"text/plain"
],
"outputModes": [
"application/json"
]
},
{
"id": "write-product-copy",
"name": "Write Converting Product Copy",
"description": "Write product descriptions, landing page copy, or marketing messages that convert \u2014 using social proof, loss framing, anchoring, and scarcity signals.",
"tags": [
"copywriting",
"conversion",
"product",
"marketing",
"persuasion"
],
"examples": [
"Write a product description for our SaaS tool that increases trial signups",
"Rewrite this landing page to convert better",
"Write persuasive copy for our pricing page"
],
"inputModes": [
"text/plain"
],
"outputModes": [
"application/json"
]
},
{
"id": "optimize-pricing",
"name": "Optimize Pricing Strategy",
"description": "Design price framing, anchoring, and tier structure to maximize willingness to pay. Includes decoy pricing analysis and value articulation.",
"tags": [
"pricing",
"anchoring",
"willingness-to-pay",
"tiers",
"revenue"
],
"examples": [
"How should we structure our pricing tiers?",
"Optimize our pricing page for higher ARPU",
"Design a pricing strategy with decoy options"
],
"inputModes": [
"text/plain"
],
"outputModes": [
"application/json"
]
},
{
"id": "predict-churn",
"name": "Predict Customer Churn",
"description": "Identify which customers are about to leave and why. Returns churn risk scores with behavioral drivers and retention interventions.",
"tags": [
"churn",
"retention",
"subscription",
"loyalty",
"engagement"
],
"examples": [
"Which users are most likely to cancel?",
"Predict churn risk for our subscriber base",
"Why are customers leaving after month 3?"
],
"inputModes": [
"text/plain",
"application/json"
],
"outputModes": [
"application/json"
]
},
{
"id": "personalize-approach",
"name": "Personalize Sales Approach",
"description": "Segment customers by decision-making style and recommend tailored approaches for each segment. Returns behavioral personas with intervention strategies.",
"tags": [
"personalization",
"segmentation",
"personas",
"targeting",
"UX"
],
"examples": [
"How should we personalize our onboarding for different user types?",
"Segment our customers by how they make purchase decisions",
"What decision-making styles do our users have?"
],
"inputModes": [
"text/plain",
"application/json"
],
"outputModes": [
"application/json"
]
},
{
"id": "add-social-proof",
"name": "Add Social Proof Signals",
"description": "Design social proof elements \u2014 what others bought, reviews, peer behavior signals. Analyzes descriptive vs injunctive norms for maximum impact.",
"tags": [
"social-proof",
"norms",
"trust",
"reviews",
"conversion"
],
"examples": [
"What social proof should we add to our product pages?",
"Design social proof for our checkout flow",
"How do we use peer behavior to increase signups?"
],
"inputModes": [
"text/plain"
],
"outputModes": [
"application/json"
]
},
{
"id": "run-experiment",
"name": "Design A/B Experiment",
"description": "Design a rigorous A/B test with sample size calculations, metrics, treatment arms, and statistical power analysis.",
"tags": [
"experiment",
"A/B-test",
"statistics",
"measurement",
"validation"
],
"examples": [
"Design an A/B test for our new checkout flow",
"How many users do we need for a valid experiment?",
"Set up an experiment to test our pricing change"
],
"inputModes": [
"text/plain",
"application/json"
],
"outputModes": [
"application/json"
]
},
{
"id": "ethics-check",
"name": "Ethics & Dark Pattern Audit",
"description": "Audit a design, intervention, or recommendation for dark patterns, manipulation, and autonomy violations. Returns ethics score with specific fixes.",
"tags": [
"ethics",
"dark-patterns",
"compliance",
"trust",
"audit"
],
"examples": [
"Is our urgency messaging manipulative?",
"Audit our checkout for dark patterns",
"Check if our nudges are ethical"
],
"inputModes": [
"text/plain"
],
"outputModes": [
"application/json"
]
}
]
}