Agent View
The site as an AI agent gets it, beside the site as a person sees it. What it can read, what it has to cut away, and what it never sees.
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See How to Defend the HMDA Fields Examiners Check First
A 30-minute live demo for bank compliance teams. See it run on a sample loan file, with live Q&A.
Examiners check HMDA accuracy before they scope fair lending, so errors in a few fields (action taken, dates, race and ethnicity, loan purpose, denial reasons) quietly widen your exposure. After a quick look at those fields, you will see each one validated against its source document, across the full population, not a sample.
What you will see
- The fields examiners prioritize, and why errors there raise fair lending risk.
- The validation running live: every field checked against its source document, across the full population, before submission.
- A defensible audit trail that shows where any reported value came from.
Examiners don’t wait for your year-end scrub, and sampling leaves gaps all year.
BanksCredit UnionsOutsourced ProcessorsAlternative LendersIT OperationsInformation SecurityRisk & ComplianceLending & Loan Operations
See How to Defend the HMDA Fields Examiners Check First A 30-minute live demo for bank compliance teams. See it run on a sample loan file, with live Q&A. Examiners check HMDA accuracy before they scope fair lending, so errors in a few fields (action taken, dates, race and ethnicity, loan purpose, denial reasons) quietly widen your exposure. After a quick look at those fields, you will see each one validated against its source document, across the full population, not a sample. **What you will see** - **The fields examiners prioritize,** and why errors there raise fair lending risk. - **The validation running live:** every field checked against its source document, across the full population, before submission. - **A defensible audit trail** that shows where any reported value came from. Examiners don’t wait for your year-end scrub, and sampling leaves gaps all year.
No h1: Give each one h1 that says what the page is.
The figures
46 pages, set against the 167,893 sites with figures and 648 in software.
- Readablehigher is better
- 100%46 of 46 pages
- All 167,893 sitesBetter than 47%648 software sitesBetter than 47%
Pages whose own text is in the HTML the server sends, enough of it for their kind.
- Cleanhigher is better
- 44%middle page
- All 167,893 sitesBetter than 23%648 software sitesBetter than 13%
Of the words before script, the share that are the page and not menus, footer and forms.
- Costshown, never scored
- 36,968tokens
- About 804 a page
To read every page once. A long page that answers well is not worse for it. Tokens are estimated at about 4 characters each.
How its pages spread on Clean
46 pages measured.
Against its rivals
The sites nearest it by what they do, with figures. Close means similar work, not better.
| Readinghigher is better | Costnot scored | ||
|---|---|---|---|
| Site | Readable | Clean | Tokens a page |
| continuous.comThis site, 46 pages | 100% | 44% | 804 |
| dedicatted.com97 pages | 98% | 80% | 4,339 |
| identifi.net100 pages | 97% | 48% | 1,206 |
| altaflow.com100 pages | 100% | 63% | 1,796 |
First of 4 on Readable, fourth on Clean.
AI crawler access
What robots.txt says to each, as read on October 24, 2026. 8 of 8 may read the site.
| Crawler | robots.txt says | Used for | The line that decides |
|---|---|---|---|
| GPTBotOpenAI | Not named | Training models | Falls to User-agent: * |
| OAI-SearchBotOpenAI | Not named | ChatGPT search | Falls to User-agent: * |
| ChatGPT-UserOpenAI | Not named | A page a person asks about | Falls to User-agent: * |
| ClaudeBotAnthropic | Not named | Training models | Falls to User-agent: * |
| Claude-SearchBotAnthropic | Not named | Claude’s search | Falls to User-agent: * |
| PerplexityBotPerplexity | Not named | Perplexity search | Falls to User-agent: * |
| Google-ExtendedGoogle | Not named | Training Gemini | Falls to User-agent: * |
| bingbotMicrosoft | Not named | Bing and Copilot | Falls to User-agent: * |
Every AI crawler mttr checks may read it. Not named means the rules for every crawler apply.
Every page
Worst first. Each opens in Agent View at the top.
8 pages of 46 lose something. Pick a loss to see what to do about it.Open the action plan
Show where it stands
A card, a badge and a line, each with the figures.
A badge for the site
<a href="https://mttr.si/agent-view/continuous.com"><img src="https://mttr.si/agent-view/continuous.com/badge.svg" alt="Agent View: 100% readable, 44% clean" height="20"></a>
The figures in one line
continuous.com in Agent View: 100% of pages readable, better than 47% of software sites; 44% clean; 36,968 tokens to read all 46 pages. mttr.si/agent-view/continuous.com
What to do next
Fix the pages, let an agent do it, and read the site again to see the figures move.
Connect your agent
Claude Code, Codex or Cursor reads the site’s fixes over MCP, fixes a page in your code, has mttr read it again, and checks the fix landed.
https://mttr.si/mcpSet up an agent
Claim the site
Prove it is yours with a DNS record or a file. Your agent can then reach its reads and jobs, and set each change to wait for your approval.
Claim the siteDrawn from the HTML mttr fetched on its read of October 24, 2026, not a screenshot, and only from the 46 pages read. An assistant’s own reader may differ. Tokens are estimated at about 4 characters each. A site an agent reads well is not a site an assistant cites.