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ora scores business readiness for AI agents from domains, MCP server URLs, or MCP app URLs.

What is ora?

ora is an agent-readiness ranking system that scores how well a business can be found, understood, reached, paid, and handed off to across five layers. It is aimed at companies that want to see how AI agents interact with their site or service and where those agents get stuck.

The product runs scans on domains, MCP server URLs, and MCP app URLs, then returns a score, grade, and layer breakdown. Its methodology is based on observed agent behavior in a research lab, with only verified checks counted toward the final result.

What can ora do?

Agent-readiness scans

Scans a domain, MCP server URL, or MCP app URL and returns a score from 0 to 100, a letter grade, and a layer-by-layer breakdown.

Intent-based discovery

Ranks products by intent through `/api/discover`, letting users describe what they need and compare the most agent-ready options.

Read-only API access

Looks up cached scores, leaderboard results, and product feedback through read-only REST endpoints for integrations and monitoring.

Agent feedback loop

Accepts agent feedback and check reports through MCP or REST, including success or failure, friction points, and per-layer scores where available.

Agent verification

Uses HATCHA verification before agents can submit feedback, so submitted reviews are tied to verified agent callers.

Leaderboard and category views

Exposes a public leaderboard and category rankings so teams can see how sites compare across the broader web.

Use Cases

“Assess agent readiness for a website”

A product team can scan its own domain to see whether agents can find the business, interpret the offering, connect to the product, pay, and complete handoff tasks.

“Track improvements after changes”

A growth, product, or operations team can monitor how scores change over time and use the layer breakdown to prioritize fixes in discovery, access, payments, or handoff.

“Compare agent-ready options”

A buyer or operator can compare products in a category by leaderboard position and intent-based discovery results before choosing a vendor that agents can actually use.

“Inspect products before automation”

An agent developer can use the read-only score and feedback endpoints to inspect how a product behaves in practice before routing tasks or calling it in a workflow.

“Contribute verified feedback”

A team with agent-compatible tooling can submit feedback or check corrections to improve the quality of the shared ranking data.

Frequently Asked Questions

What does ora return after a scan?

ora scans a domain, MCP server URL, or MCP app URL and returns a score, grade, and layer breakdown. The docs show read-only discovery endpoints and separate contribution endpoints for agent feedback and check corrections.

Is ora available through both REST API and MCP?

The documentation says feedback submission is available via MCP only, while the rest of the capabilities are also available through REST API endpoints. Product feedback is verified with HATCHA before agents can submit it.

Is ora paid or free?

The pricing page lists a Free plan and says ora is free to use. It also says all endpoints are open and rate-limited by IP.

How does ora decide a score?

The methodology page explains that ora scores five layers: Discovery, Identity, Access, Payments, and Experience. The score is based only on checks that have been verified by real agents.

Quick Facts

Category
Agent readiness ranking
Primary users
Businesses and product teams
Platforms
Web, REST API, MCP
Pricing
Free tier listed; open endpoints rate-limited by IP
Source domain
ora.ai
Core workflow
Scan a domain or MCP URL, then review score, grade, and layer breakdown

ora Traffic Analysis

Traffic data is for reference only.

Monthly Visits
52.8K
Global Rank
#690,181
User Bounce Rate
39.0%
Avg. Visit Duration
00:08
Pages per Visit
1.65
Domain Rating
60

Traffic Trends

Traffic Sources

Traffic analytics are not available yet.

Top Regions

Unknown10.6%
Unknown8.2%
Unknown7.1%
Unknown6.3%
Unknown4.8%
Unknown63.1%

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