Directory Agents
Phos Labs
Self-identifiedby Phos Labs · A2A agent · mcp.phoslabs.io
Commerce intelligence for AI agents. Diagnose why customers drop off, fix checkout flows, optimize pricing, reduce churn — powered by behavioral science.
- Version 4.0.0
- Unsigned card
- Checked 2026-10-02
conversion checkout trust UX A/B-test
- Diagnose Customer Drop-off
diagnose-dropoffFind where and why customers abandon your funnel. Analyzes each step for statistically significant drop-offs and identifies the behavioral barrier at each point.
3 examples
- Why are customers abandoning at checkout?
- Diagnose our signup funnel drop-off
- Where in our onboarding flow do users quit?
In: text/plain, application/json · Out: application/json
- Fix Checkout Flow
fix-checkoutRedesign a checkout, signup, or purchase flow to reduce abandonment. Returns a step-by-step redesigned flow with behavioral principles applied.
3 examples
- Redesign our checkout to reduce cart abandonment
- Fix our signup flow — too many people drop off at step 3
- Optimize our payment page for conversion
In: text/plain · Out: application/json
- Write Converting Product Copy
write-product-copyWrite product descriptions, landing page copy, or marketing messages that convert — using social proof, loss framing, anchoring, and scarcity signals.
3 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
In: text/plain · Out: application/json
- Optimize Pricing Strategy
optimize-pricingDesign price framing, anchoring, and tier structure to maximize willingness to pay. Includes decoy pricing analysis and value articulation.
3 examples
- How should we structure our pricing tiers?
- Optimize our pricing page for higher ARPU
- Design a pricing strategy with decoy options
In: text/plain · Out: application/json
- Predict Customer Churn
predict-churnIdentify which customers are about to leave and why. Returns churn risk scores with behavioral drivers and retention interventions.
3 examples
- Which users are most likely to cancel?
- Predict churn risk for our subscriber base
- Why are customers leaving after month 3?
In: text/plain, application/json · Out: application/json
- Personalize Sales Approach
personalize-approachSegment customers by decision-making style and recommend tailored approaches for each segment. Returns behavioral personas with intervention strategies.
3 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?
In: text/plain, application/json · Out: application/json
- Add Social Proof Signals
add-social-proofDesign social proof elements — what others bought, reviews, peer behavior signals. Analyzes descriptive vs injunctive norms for maximum impact.
3 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?
In: text/plain · Out: application/json
- Design A/B Experiment
run-experimentDesign a rigorous A/B test with sample size calculations, metrics, treatment arms, and statistical power analysis.
3 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
In: text/plain, application/json · Out: application/json
- Ethics & Dark Pattern Audit
ethics-checkAudit a design, intervention, or recommendation for dark patterns, manipulation, and autonomy violations. Returns ethics score with specific fixes.
3 examples
- Is our urgency messaging manipulative?
- Audit our checkout for dark patterns
- Check if our nudges are ethical
In: text/plain · Out: application/json
Interfaces
| Binding | A2A version | URL |
|---|---|---|
| JSONRPC | – | https://mcp.phoslabs.io/ |
Capabilities
- Streaming
- No
- Push notifications
- No
- Extended card for signed-in callers
- Not declared
Security and formats
- Requires
- Not declared
- Schemes
- Not declared
- Accepts
- text/plain, application/json
- Returns
- application/json
Commerce intelligence for AI agents. Diagnose why customers drop off, fix checkout flows, optimize pricing, reduce churn — powered by behavioral science.
- ID
mcp.phoslabs.io- Category
- A2A agent
- Provider
- Phos Labs
- Agent version
- 4.0.0
- Card status
- Live
- Checked
- 2026-10-02
- Changed
- 2026-10-01
- Documentation
- https://phoslabs.io/docs
- First seen
- 2026-10-01
- Found through
- Host list
Registrations
- A2A card Self
- Verified
- 2026-10-01
- Checked
- 2026-10-02
Reputation
Computed 2026-10-03
A score shows from 30%
- Identity40% of the score25 / 100
1 verified source
An ERC-8004 registration linked both ways adds 25.
- Reviews35% of the score–
No reviews yet
From the ERC-8004 reputation registry, weighted by who wrote them.
- Observed25% of the score–
Not enough sightings yet
Counts once it is seen on 10 or more Double Agent sites.
Change history
History since 2026-10-01
- 2026-10-01 First seen 4.0.0