papermark.io agentic web readiness

A Manifest validity: 67% (2/3 valid, 3 errors, 8 warnings) · Surface score 46 (coverage 30 + breadth 6 + conformance 10). 4 standards observed across 3 of 9 steps; 3 manifests present, 3 errors, 8 warnings. Checked 2026-10-25 19:17:54 UTC. This grades the agent-facing files published at papermark.io, not the organisation's products.

Badge for a README: ![agentic web grade](https://agenticweb.wiki/badge/papermark.io.svg) · JSON · Markdown · Re-check now

Coverage by step

Manifests

{
  "$schema": "https://static.modelcontextprotocol.io/schemas/v1/server-card.schema.json",
  "name": "io.papermark/papermark",
  "title": "Papermark",
  "version": "1.0.0",
  "description": "Papermark and what it offers to agents",
  "websiteUrl": "https://papermark.io/",
  "icons": [
    {
      "src": "https://papermark.io/icon.png",
      "mimeType": "image/png",
      "sizes": [
        "180x180"
      ]
    }
  ],
  "remotes": [
    {
      "type": "streamable-http",
      "url": "https://papermark.io/mcp",
      "supportedProtocolVersions": [
        "2025-11-25",
        "2025-06-18",
        "2025-03-26"
      ]
    }
  ]
}

What an agent can call

• MCP endpoint https://mcp.papermark.com/mcp (streamable-http), tools: datarooms, documents, folders, links, views, visitors — from /.well-known/mcp.json
• llms.txt: Papermark - secure open-source virtual data room and document sharing, 60 links, llms-full.txt present

Standards observed

• llms.txt https://papermark.io/llms.txt
• Agent Skills https://papermark.io/.well-known/agent-skills/index.json
• MCP Server Cards (SEP-2127)
• Model Context Protocol

Cheapest next steps

1. AI Crawler User-Agent Tokens in robots.txt: state what AI crawlers may do in robots.txt you already serve
   Generate starter /robots.txt for papermark.io

   # search and answer-engine crawlers
   User-agent: Googlebot
   User-agent: bingbot
   User-agent: OAI-SearchBot
   User-agent: Claude-SearchBot
   User-agent: PerplexityBot
   User-agent: ExaSearchBot
   User-agent: TavilyBot
   Allow: /
   
   # model-training crawlers: choose Allow or Disallow and keep it explicit
   User-agent: GPTBot
   User-agent: ClaudeBot
   User-agent: Google-Extended
   User-agent: Applebot-Extended
   User-agent: CCBot
   User-agent: meta-externalagent
   User-agent: Bytespider
   Disallow: /
   
   # user-triggered fetchers (a person asked for this page)
   User-agent: ChatGPT-User
   User-agent: Claude-User
   User-agent: Perplexity-User
   Allow: /
   
   # default fallback for unlisted crawlers (RFC 9309 §2.2.1: specific groups above take precedence over *)
   User-agent: *
   Allow: /
   Disallow: /account/
   Content-Signal: ai-train=no, search=yes, ai-input=yes
   
   Sitemap: https://papermark.io/sitemap.xml
   Agentmap: https://papermark.io/.well-known/ard.json
   

2. Agentic Resource Discovery: a catalog at /.well-known/ard.json lets agents discover your APIs, MCP servers and data in one fetch (emerging spec — publish only if you run the underlying endpoints; generate via MCP template({"id": "ard", "domain": "papermark.io"}))
3. AGENTS.md: instructions for coding agents working on or with the site
   Generate starter /AGENTS.md for papermark.io

   # Papermark for agents
   
   Papermark and what it offers to agents
   
   ## Read the data, not the HTML
   - Index: https://papermark.io/llms.txt
   - API description: https://papermark.io/openapi.json
   - Discovery catalog: https://papermark.io/.well-known/ard.json
   
   ## Calling
   - MCP server (Streamable HTTP): https://papermark.io/mcp
   - Rate limits and authentication: describe them here
   
   ## Conventions
   - How to cite or attribute content
   - What not to do (no scraping of /account/, no automated sign-ups, etc.)
   
   Contact: security@papermark.io
   

Agent pre-flight & practical readiness

Preferred mode: mcp (fallback agent_skills). Call the MCP endpoint at https://mcp.papermark.com/mcp first. Do not scrape the HTML when a tool can answer.

Fetch: POST https://mcp.papermark.com/mcp POST a JSON-RPC tools/list, or server/discover on the 2026-07-28 revision, before reading HTML.

Practical readability: exa_parsable yes, markdown negotiation no, render none, HTML quality none.

AI crawler posture: unspecified · Assistant access: OpenAI unspecified, Anthropic unspecified, Google unspecified, Perplexity unspecified, Exa unspecified.

Readiness subscores: reach 0/20, readability 10/30, capabilities 21/35, conformance 10/15, total 41/100, grade C.

How the grade is made

+10 per lifecycle step with a standard observed, counting manifests that validate or that the probe rule for the standard recognises (a valid MCP server card counts for MCP; max 90); +3 per observed standard beyond the first two (max 15); conformance +15 (+10 with one manifest) minus 5 per agent-facing manifest with errors, never below 0 (security.txt is reported, not scored). Lifecycle grade: A+ >= 60, A >= 45, B >= 30, C >= 20, D >= 10. Strict 100-point composite (subscores.total_100 / subscores.grade_100): reach_and_policy (20) + readability (30) + capabilities (35) + conformance (15), where A+ >= 80, A >= 65, B >= 45, C >= 28, D >= 15. Errors are specification violations; warnings are best-practice gaps. Only public paths are fetched, without credentials, so standards that need a login (skipped in the probe) are not counted either way. The same report is the validate_manifest tool on the MCP server.

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