NanoParse MCP

The easiest way for AI agents to read the web. NanoParse MCP is a hosted Model Context Protocol server at https://nanoparse.app/mcp that turns any URL into clean Markdown your agent can actually use — with machine-readable trust signals attached.

No install. No wallet key. No .env. Connect once and your agent can read the web.

Why agents choose NanoParse over Firecrawl or Jina Reader: every page comes back with Litmus — 20 machine-readable signals that tell your agent whether to trust and cite the page before it reads a word. Pay $0.005 per parse via x402 once the free tier runs out. Full JavaScript/SPA rendering, native GFM tables. No account, no subscription, no API key.

Quick start

Add this to your MCP client config:

{
  "mcpServers": {
    "nanoparse": {
      "type": "http",
      "url": "https://nanoparse.app/mcp"
    }
  }
}

Restart your client, and your agent auto-discovers two tools:

• nanoparse_fetch(url) — fetch any web page and get clean Markdown back
• nanoparse_status() — check free quota, wallet and USDC balance before spending

That's it. Your agent can now fetch pages from inside any MCP-compatible client (Claude, Cursor, Hermes, Windsurf, and others).

What you get

nanoparse_fetch renders the page in a real headless browser (JavaScript and all), isolates the actual article from nav, ads, and cookie banners, and returns:

• Clean Markdown — GFM tables, callouts, math, and footnotes preserved
• Metadata — title, author, dates, language, word count, OpenGraph, schema.org JSON-LD
• Litmus — machine-readable signals your agent can reason over: source authority, freshness, structural trust, content density, syndication and paywall detection

nanoparse_status tells your agent exactly where it stands: free parses remaining, wallet and USDC balance, and whether the next call will succeed without payment. Agents call it before spending.

Real output

Every parse returns markdown + metadata + litmus. Here is the full litmus object from a real parse of an arXiv paper (examples/example-output.json — markdown truncated there, litmus complete and unmodified):

{
  "litmus": {
    "source": { "type": "academic", "authority": 0.85, "rationale": "academic source" },
    "freshness": { "published": null, "age": "unknown", "stale_warning": "No publish date found — verify independently", "modified": null, "has_correction": false, "correction_note": null },
    "tldr": "Abstract page for arXiv paper 2401.14295: Demystifying Chains, Trees, and Graphs of Thoughts",
    "key_figures": [ { "value": "2,247", "context": "Thu, 25 Jan 2024 16:34:00 UTC (2,247 KB)" } ],
    "structural_trust_score": { "level": "medium", "trust_factors": ["High-authority source", "No publish date found — verify independently"] },
    "hedge_language": { "density": "low", "markers_found": 0, "per_500_words": 0 },
    "content_density": { "thin_content": false, "substantive_sentence_ratio": 0.76 },
    "syndication": { "detected": false, "note": null },
    "paywall": { "detected": false, "note": null },
    "content_type": "academic_paper",
    "reading_time": { "minutes": 4 },
    "is_archived": { "archive_org": true, "archive_is": false },
    "outbound_authority": { "score": 0.54, "top_domains": ["ui.adsabs.

... [View full README on GitHub](https://github.com/nanoparse-dev/nanoparse-mcp#readme)