Agent readiness scanner

Paste your website, REST API or MCP endpoint to get a 0 to 100 score for AI agent readiness, with fix prompts.
Free scan, no signup

10 free scans a day, no sign-up. Paste a website, REST API or MCP endpoint to get a 0 to 100 agent readiness score. Try with

  • No credit card
  • 44 checks, six categories
  • Fix prompts included
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Top 100 sites

The highest public scans. Switch a category to rank by that score; Live Agent Run ranks by what real agent frameworks managed on the site.

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Over 122,000 agent readiness scans run on StartupHub.ai

  • Google
  • NVIDIA
  • Sequoia Capital
  • The scan runs 44 checks in six categories: discoverability, content, access control, capabilities, commerce, quality and returns a 0 to 100 score with a letter grade. Every failed check has a paste-ready fix prompt for coding agent Claude Code or Cursor.

vercel.comScan

Grade B

71 / 100

FCA+
Discoverability
82
Content
81
Access control
76
Quality
72
Capabilities
61
Commerce
57

Why agent readiness is its own score

Be found by agents, not only by search

SEO is about ranking in search results people click. Agent readiness is about whether an AI agent can find, read and use the site.

Fix prompts, not a report

Every failed check includes a paste-ready fix prompt for Claude Code or Cursor. Apply fixes and scan again to confirm the new score.

See where you stand

A public leaderboard on the page ranks scanned sites by overall score, by each category and by Live Agent Run results.

How the StartupHub.ai scanner works

  1. Step 1

    Paste a URL

    Paste a URL for your own site, a competitor, a REST API or an MCP endpoint, free with no signup.

  2. Step 2

    The scan runs

    The scan runs 44 checks in six categories and returns a 0 to 100 score with a letter grade. It probes robots.txt, sitemap.xml, llms.txt, openapi.json, the MCP server card and the agent payment discovery files.

  3. Step 3

    The Live Agent Run

    The Live Agent Run is optional and is turned on with a switch next to the box. It starts six frameworks on our hardware: Claude Code, Codex CLI, OpenCode, Cursor, Gemini CLI and a plain model to answer seven questions.

  4. Step 4

    Read the result and fix it

    The grade A+ is 90 or above, A 80, B 70, C 60, D 40 and F under 40. Every failed check has a paste-ready fix prompt for coding agent Claude Code or Cursor.

What people use the Agent Readiness Scanner for

You want to know where AI agents struggle on your site. Run the scanner to get a score from 0 to 100 with a letter grade and focus on the lowest of the six category scores to see what to fix first.

stripe.comScanned 22 Oct 2026
Overall
57 / 100 (D)
Discoverability
76
Access control
76
Content
69
Quality
69
Commerce
40
Capabilities
27

Where agents get stuck

Weakest: Capabilities, 27 · Next: Commerce, 40 · Scanned: Oct 2026

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3 full scans a month

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One key

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API docs

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Agent readiness wherever you work

It runs from an AI agent over MCP, the REST API, a monitor that rescans and a badge on the site.

MCP and AI agents

The scan_agent_readiness tool runs the same scan over MCP with no key in Claude, ChatGPT, Cursor and n8n.

onboarding prompt

> Add the MCP server https://www.startuphub.ai/api/v1/mcp (name: StartupHub.ai) and use its scan_agent_readiness tool to score a site for AI agents and give me the fix prompts.

Claude Code

> Is vercel.com ready for AI agents?

● startuphub - scan_agent_readiness (MCP)

  ⎿ score: 71 / 100, grade B, commerce 57, capabilities 61

● vercel.com scores 71, grade B. The weakest category is commerce; fix prompts are attached.

Claude Code

Claude Code connects to our MCP server once and can then scan any site mid-task.

Set up in Claude Code
POST /api/v1/agent-readiness200 OK
{
  "url": "https://vercel.com",
  "score": 71,
  "grade": "B",
  "categories": { "discoverability": 82, "commerce": 57 }
}

Agent readiness API

One REST call returns the score, grade, six category scores and every check as JSON for 1 credit or $0.001 with x402 and no key.

Read the API docs

vercel.com, rescanned

  • llms.txtPass
  • MCP server cardCheck
  • Score71 / 100
Alert when a check stops passing

Monitor

StartupHub Monitor rescans a site and reports when a check stops passing.

Set up a monitor

vercel.com

  • Agent readinessB, 71 / 100
  • Live Agent RunSix frameworks
Badge links to the report

Agent readiness badge

A site that scores well can show the StartupHub.ai agent readiness badge linking to its report.

Scan to get the badge

Since April 2026, StartupHub.ai has run over 122,000 agent readiness scans on websites, APIs and MCP servers

Scans run

+122,000

Scans, running total

Last 30 days

+94,000

Domains scanned

44

Checks in every scan

6

Agent frameworks in the Live Agent Run

Find more data on the companies you care about

StartupHub.ai profiles AI startups, investors and people with funding, team and tech stack data on the site, over the REST API and over MCP.

  • REST API and MCP
  • Pay as you go
  • Free to start

Platform

Find leads on StartupHub.ai

Search startups, investors and people then find and verify their work emails. Start free with no account needed.

Search the directory

Data

Use our data API

Put startup, investor, people and email data inside your product over a REST API and pay as you go per call.

Read the API docs

AI

Add StartupHub.ai to AI

Give AI assistants and agents our data over MCP. The MCP connection works in Claude, ChatGPT, Cursor and n8n.

Connect StartupHub.ai to AI

Live Agent Run: real AI agents on your site

The Live Agent Run starts six agent frameworks on StartupHub.ai hardware and points them at the site: Claude Code, Codex CLI, OpenCode, Cursor, Gemini CLI and a plain model with a single fetch tool as a control. They fetch pages, read the llms.txt file, the MCP server card and the API docs, and call what they find. Nothing is simulated. The scan says whether the signals are present and the Live Agent Run shows what an agent actually finds.

  • Claude CodeAnthropic
    4.7answers, avg

    223 runs · 5.3 fetches · 45 s

  • Codex CLIOpenAI
    4.8answers, avg

    223 runs · 6.1 fetches · 32 s

  • OpenCodeOpen source
    4.8answers, avg

    223 runs · 5.4 fetches · 20 s

  • CursorCursor
    5.2answers, avg

    209 runs · 9.0 fetches · 63 s

  • Gemini CLIGoogle
    6.1answers, avg

    25 runs since 14 Oct · 7.3 fetches · 163 s

  • Plain model with a fetch toolControl
    2.9answers, avg

    404 runs · 1.3 fetches · 2 s

1,307 runs on 217 sites since 1 October 2026, measured 22 October 2026.

The questions every agent gets

Every framework answers the same seven questions about the site. When the scan finds a way to pay, it also asks whether an agent could pay.

  1. 1What they do
  2. 2What it costs
  3. 3Why to believe them
  4. 4Who is behind it
  5. 5What happens to your data
  6. 6Whether there is an API
  7. 7What they sell
  8. 8Whether an agent could pay

Fetched as 17 AI clients

The same run also fetches the page as 17 named AI clients and reports which ones got it. That covers answer engines, search crawlers and training crawlers.

Answer engines
ClaudeChatGPTPerplexityLe ChatDuckDuckGo AIMeta AI
Search crawlers
Claude search indexOpenAI search indexGooglebotBingbotApplebotAmazonbot
Training crawlers
GPTBotClaudeBotCCBotMeta-ExternalAgentBytespider

Since 1 October 2026 the Live Agent Run has finished 1,307 runs on 217 sites. The plain model with a fetch tool found 2.9 answers on average and made about one fetch per run. The agent frameworks found more and made five to nine fetches, so the harness decides how much of a site an agent finds, not only the model.

On sites that scored 80 or more, the agent frameworks found 5.3 answers on average. Sites that scored 60 to 79 found 4.6 on average. A higher scan score went with more answers found, but the link is modest, which is why the run exists alongside the scan.

A free account gets three Live Agent Runs a month. Pro Mini from $5 a month removes the cap and keeps watching the site.

REST · MCP · x402

One scan call. Every signal an agent needs.

One call returns the score, grade, six category scores and every check fired as JSON, so CI, monitoring cron or a Claude agent over MCP can act. The Live Agent Run results for a domain come from the same API, one row per framework.

  • POST /api/v1/agent-readiness: score, grade, 6 category scores and every check as JSON
  • MCP tool scan_agent_readiness on the StartupHub.ai MCP server
  • Live Agent Run results per domain: GET /api/agent-readiness/agent-task?domain=
  • Bearer key: 1 credit a scan, 1,500 free credits a month
  • No key: $0.001 a scan over x402
POST /api/v1/agent-readiness200
57stripe.com, grade D
  • Discoverability76
  • Content69
  • Access control76
  • Capabilities27
  • Commerce40
  • Quality69
Same JSON from the MCP tool scan_agent_readiness. Scanned 22 October 2026.

What is agent readiness?

Agent readiness is whether an AI agent can find, read and use a site without a person. The StartupHub.ai Agent Readiness scanner is free and needs no signup. Paste a website, REST API or MCP endpoint and it returns a score from 0 to 100 with a letter grade. It runs 44 checks in six categories and every failed or warned check comes with a fix prompt for Claude Code or Cursor.

113,581
scans run
93,652
sites scored
50/100
median, last 45 days
79%
graded D or F

StartupHub.ai has run 113,581 scans covering 93,652 sites. Of the 4,328 sites scanned in the last 45 days the median score was 50 out of 100, 79% graded D or F and 1.8% reached A or A+.

The six agent readiness categories

The scan covers six categories. A passed check earns its full weight, a warning earns 40 percent, a failed check earns nothing and a skipped check is left out. The weighted total is 0 to 100: A+ is 90 or above, A is 80, B is 70, C is 60, D is 40 and F is below 40.

1

Discoverability

This category checks whether an agent can discover the site. It verifies a robots.txt file, a declared sitemap, a Link response header that follows RFC 8288, a rel="describedby" link to llms.txt and JSON-LD SearchAction for the on-site search URL. AGENTS.md or CLAUDE.md counts as an optional pass for code repositories.

2

Content

This checks if agents can read content. It verifies markdown for Accept: text/markdown, llms.txt in v2 shape with H1, summary and links with a short note each, that those links open, optional llms-full.txt, that page text is in raw HTML, that headline and first facts sit in first 50 KB and that HTML stays under about 300 KB because many AI fetchers stop after a few hundred KB.

3

Access control

This category checks whether AI agents can access the site. It verifies that Claude-User, Claude-SearchBot, ChatGPT-User and OAI-SearchBot are not blocked at the site root, that robots.txt has explicit rules for AI crawlers and that Content Signals are declared. It also fetches the page as GPTBot, ClaudeBot, PerplexityBot, meta-externalagent, Bingbot and Googlebot to confirm each one succeeds, while training crawlers can stay blocked.

4

Capabilities

This category checks what an agent can do beyond reading. It verifies an MCP server card, an API catalog that follows RFC 9727, an OpenAPI document, an Agent Skills index, OAuth discovery and agentic registration so an agent can get scoped access without a person provisioning keys. An A2A agent card is optional and never lowers the score.

5

Commerce (agent payments)

This category checks whether an agent can pay. It verifies x402, MPP, AP2 or ACP discovery, x402-mesh, Offer or Product JSON-LD with a price, a payment link reachable from the homepage and whether /checkout or /pricing exists. Each protocol is probed on its own and a site with no paid endpoints is not penalised for MPP.

6

Quality

This category checks quality signals that help an answer engine quote the page. It verifies complete Open Graph tags, a single H1, image alt text, a meta description, a canonical URL, JSON-LD, entity JSON-LD in the first 30 KB, RateLimit headers that follow RFC 9331 and security.txt.

Agent readiness report example: stripe.com scores 57 out of 100

The scanner checked stripe.com on 22 October 2026. It scored 57 out of 100, grade D, and you can re-run the same check in the scanner above.

Agent readiness category scores for stripe.com, scanned 22 October 2026
CategoryScoreChecks passed
Discoverability76 / 1002 of 5
Content69 / 1005 of 8
Access control76 / 1002 of 4
Capabilities27 / 1000 of 6
Commerce40 / 1001 of 6
Quality69 / 1006 of 9
Overall57 / 100Grade D

Of 44 checks, 16 passed, 17 warned, 5 failed and 6 did not apply. The five failures were no markdown on Accept: text/markdown, no MCP server card, no Offer or Product JSON-LD on the homepage, two H1 tags, and 27 of 31 images without alt text. The homepage HTML is 670 KB and costs about 170,897 tokens to read as HTML. The markdown version would cost about 3,926, which is 98% fewer. stripe.com passes the llms.txt checks, with 119 linked files.

What the Live Agent Run found on stripe.com

Live Agent Run on stripe.com, 14 October 2026, 8 questions
AgentAnswers foundFetchesTime
Cursor8 of 8895 s
Claude Code6 of 8879 s
OpenCode6 of 8726 s
Codex CLI5 of 8875 s

The Live Agent Run on stripe.com on 14 October 2026 asked 8 questions, because the scan found a way to pay. Cursor found all 8 answers. Claude Code and OpenCode each got 6. Codex CLI got 5. The plain model with a fetch tool was refused or got no usable text in four of its five attempts. So a site can grade D on the scan while capable agents still find most of what they need, and a simple fetcher gets turned away.

How to improve your agent readiness score

  1. 1Publish an MCP server card. This is the heaviest single check at weight 5. If you run an MCP server, publish /.well-known/mcp/server-card.json describing its tools so agents such as Claude and Cursor can discover them.
  2. 2Serve markdown to agents. Your server should return the page as markdown when a request sends Accept: text/markdown. The check carries weight 4 and on stripe.com it would cut the homepage from about 170,897 tokens to about 3,926.
  3. 3Publish llms.txt in the v2 shape. Publish /llms.txt in the v2 shape with an H1 that includes the site name, a one-line summary and links that each carry a short note, and point at it with a rel="describedby" link. Doing both passes all four llms.txt checks for the guide, link notes, links resolve and discovery link.
  4. 4Put your prices in JSON-LD. This check has weight 3. Put JSON-LD Offer or Product data with price and priceCurrency on the homepage so an agent can read your pricing without scraping the layout.
  5. 5Keep the HTML small and the facts early. Keep HTML under about 300 KB and put the H1 and first facts in the first 50 KB. Many AI fetchers stop reading after a few hundred KB, so a footer, FAQ or contact details near the bottom of a heavy page may never be read.

StartupHub.ai scanner vs Cloudflare's isitagentready.com

Cloudflare runs a free agent readiness checker at isitagentready.com. Its page lists five categories: Discoverability, Content Accessibility, Bot Access Control, Protocol Discovery and Commerce. The StartupHub.ai scanner runs 44 checks in six categories, adding Quality: Open Graph, a single H1, image alt text, JSON-LD near the top and the size of the HTML. It does two things the isitagentready.com page does not list: the Live Agent Run with six agent frameworks and fetching the page as 17 named AI clients. It adds a public leaderboard, REST API and MCP tool, and both checkers are free. Full comparison.

Common questions about the agent readiness scanner.

If you do not find your answer here, reach us from the help and contact page.

Help and contact
What is an agent readiness scanner?

An agent readiness scanner measures how well agents can use your site. The StartupHub.ai Agent Readiness scanner is free with no signup and scores a website, REST API or MCP endpoint from 0 to 100 with a letter grade. It runs 44 checks in six categories and includes a fix prompt for Claude Code or Cursor. It is also available as POST /api/v1/agent-readiness and as the MCP tool scan_agent_readiness.

Is the agent readiness scanner free?

Yes, the StartupHub.ai Agent Readiness scanner is free and needs no signup. Paste a website, REST API or MCP endpoint for a score from 0 to 100 with a letter grade. Live Agent Run is three a month on a free account, and Pro Mini from $5 a month removes the cap and keeps watching the site. POST /api/v1/agent-readiness costs 1 credit and the free plan includes 500 credits monthly.

What is the Live Agent Run?

The Live Agent Run points six agent frameworks at your site from StartupHub.ai hardware. The frameworks are Claude Code, Codex CLI, OpenCode, Cursor, Gemini CLI and a plain model with a fetch tool. They fetch pages, read llms.txt, MCP server card and API docs and call what they find with nothing simulated. Each framework answers seven questions and an eighth about payment when found. The same run checks 17 named AI clients and the Live Agent Run shows what agents actually find.

Which AI agents run in the Live Agent Run?

The Live Agent Run uses six agent frameworks. They are Claude Code from Anthropic, Codex CLI from OpenAI, OpenCode open source, Cursor, Gemini CLI from Google and a plain model with a fetch tool as a control. Cursor runs on its own backend and the other frameworks run on models StartupHub.ai chooses. Windsurf and Cline are not included because they only run inside an editor.

Why is a plain model part of the Live Agent Run?

The plain model with one fetch tool is the Live Agent Run control. It is what teams build to point an AI at a site. If it finds as much as Claude Code on a site, the framework was not the variable and the site was. Across 1,307 runs since 1 October 2026 it averaged 2.9 answers on about one fetch while the agent frameworks found between 4.7 and 5.2 on five to nine fetches.

How is the agent readiness score calculated?

The agent readiness score is a weighted total from 0 to 100 across 44 checks in six categories: discoverability, content, access control, capabilities, commerce and quality. A passed check earns full weight and a warning earns 40 percent of its weight. A failed check earns nothing and a skipped check is left out of the total. Grades are A+ 90 or above, A 80, B 70, C 60, D 40 and F below 40. A link to startuphub.ai in llms.txt adds 8 points and a missing link never lowers score.

Does my site need an MCP server to score well?

No. MCP is part of one category, capabilities, out of six. A content site with good discoverability, content, access and quality scores well without one. An MCP server card matters when you have an API or a product an agent would want to call. It is the heaviest single check, weight 5.

How is agent readiness different from SEO?

They are different goals. SEO is about ranking in search results that people click. Agent readiness is about whether an AI agent can find, read and use a site without a person. That includes markdown, llms.txt, MCP and OpenAPI, machine-readable prices, and fetchers that do not run JavaScript. They overlap on JSON-LD, sitemaps, Open Graph and a single H1, which both use.

Can I run the scanner from code or an AI agent?

Yes, you can run the scanner from code or AI agent. POST /api/v1/agent-readiness takes a URL and returns score, grade, six category scores and every check as JSON. The call costs 1 credit with a Bearer key and the free plan includes 500 credits monthly, and agents with no key can pay $0.001 a scan over x402. The same scan is the MCP tool scan_agent_readiness on the StartupHub.ai MCP server. Results are readable at GET /api/agent-readiness/agent-task?domain=, one row per framework.

Does the scanner check x402 and MPP payments?

Yes, it checks whether an agent can pay. It checks x402, MPP, AP2 or ACP discovery, x402-mesh, Offer or Product JSON-LD with a price, a homepage payment link, and /checkout or /pricing. A site with no paid endpoints is not penalised for MPP, and each protocol is probed on its own. MPP is the Machine Payments Protocol from Tempo and Stripe, detected by a WWW-Authenticate: Payment challenge or /.well-known/payment.

Where can I see how other sites score?

You can compare on the public leaderboard on this page. It ranks scanned sites by overall score, by each of the six categories, and by Live Agent Run results. A site needs runs from at least three frameworks to appear on the Live Agent Run board. Each domain has a shareable report at /agent-readiness?url= followed by the domain.

Do you keep the sites I scan?

Yes, every scan is stored with its result. The latest score for a domain can appear on the public leaderboard, and on its StartupHub.ai profile when it has one. Only public pages are fetched, and the scanner refuses private network addresses.

Make your site ready for agents

Free, no sign-up. 10 scans a day.

exowatt.com scores 41/100 (D) on agent readiness | StartupHub.ai