FakeJSON
FakeJSON's pricing page did not render numeric tier data on the scoring date. No per-unit price was found on the page; an agent has no direct way to estimate the cost of a single task. All advertised paths route to a sales conversation rather than a self-serve checkout. Public API documentation is reachable without an auth wall.
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$12
Contact sales
How FakeJSON scores on the agent-ready dimensions
Public pricing
0 / 15
Usage-based / metered
0 / 25
Self-serve checkout
0 / 15
Public API
15 / 15
Low / no minimum
0 / 10
Unauth automated payment
0 / 10
Bonus (machine-readable pricing)On top of /100 base
0 / 5
Total
15 / 100
Can an autonomous agent transact here?
Can create account
Can enter payment method
Can upgrade plan
Can buy more usage
Can cancel online
Payment methods
Unknown
Human approval recommended
Pricing transparency
contact_only
Purchase motion
sales_led
Demo required
Unknown
Quote required
Unknown
Annual commit required
Unknown
Auto-renewal mentioned
Cancellation policy visible
Refund policy visible
Invoice payment available
Unknown
Purchase orders supported
Unknown
MSA required
Unknown
DPA available
Unknown
Security review required
Unknown
Six-step check: can an agent actually buy from FakeJSON?
Discover price
Select a plan
Pay per task
Avoid a sales call
API docs without auth
Estimate cost upfront
Pros and cons for AI agents
Observational summary written by xpay from the signals captured on 2026-05-06. Not a review of the product — only of its current pricing posture for agent buyers.- Pricing is publicly visible on an indexable page — agents can read tiers without scraping past auth.
- Every advertised tier is self-serve; no tier requires scheduling a call.
- No per-unit price was advertised, so an agent has no way to estimate the cost of a single task.
- API documentation is gated or absent; an agent cannot inspect the integration surface without authentication.
- No /.well-known/ai-pricing.json or equivalent machine-readable pricing manifest is published — agents must rely on HTML scraping.
- The pricing URL did not render numeric tier data on the scoring date — possibly a router or sales-led landing page.
How FakeJSON could lift its score
Add a per-unit price (e.g. $X / 1K calls) to the pricing page so an agent can compute its own cost before committing.
| pricing_visible | false |
| headline_phrasing | JKTOK: APK Slot Online Terbaru Akses Link Alternatif Gacor Hari Ini — Situs Permainan Online & Hiburan Digital |
| tier_count | 0 |
| lowest_paid_entry_usd | null |
| free_tier | false |
| free_tier_terms | null |
| per_unit_price | null |
| annual_required | false |
| self_serve_paid_tiers | 0 |
| sales_only_tiers | 1 |
| public_api_docs_url | https://gp29.net/ |
| api_docs_auth_walled | false |
| ai_pricing_json_present | false |
| agents_txt_present | false |
| anonymous_purchase_path | false |
| per_unit_classification | null |
| usage_headline_present | false |
| custom_tier_present | false |
| agent_friendly | {"ai_pricing_json":false,"agents_txt":false,"llms_txt":false,"sitemap_xml":false,"mcp_server_card":false,"agent_skills_index":false,"x402_supported":false} |
View raw extracted page text →
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See agent-ready picksFakeJSON
15
/ 100 (rubric v1.1)API Management
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0
No
Sales-led
0 / 1
Public
No
Not published
2026-05-06
Sales-led pricing
Discovery files and protocols
Side-channel signals — informational, not part of the score. Each protocol is independent; adoption signals the publisher is thinking about agent buyers.ai-pricing.json
agents.txt
llms.txt
sitemap.xml
MCP Server Card
Agent Skills
x402 / MPP
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