nanoparse-mcp
OfficialClick on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@nanoparse-mcpfetch https://example.com/article as markdown"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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: no account, no subscription, no API key. Pay $0.01 per parse via x402 when the free tier runs out. Full JavaScript/SPA rendering, native GFM tables, and Litmus trust signals — built for agents, not humans.
Quick start
Add this to your MCP client config:
{
"mcpServers": {
"nanoparse": {
"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 backnanoparse_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).
Related MCP server: mdapi-mcp
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. Example — parsing an arXiv paper (examples/example-output.json, complete and unmodified):
{
"litmus": {
"source": { "type": "academic", "authority": 0.85, "rationale": "academic source" },
"freshness": { "published": null, "stale_warning": "No publish date found — verify independently" },
"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)" } ],
"content_type": "academic_paper",
"reading_time": { "minutes": 4 },
"hedge_language": { "density": "low", "markers_found": 0 },
"syndication": { "detected": false },
"paywall": { "detected": false }
}
}The agent learns before reading a word: this is an academic source (0.85 authority), no publish date was found (verify independently), here's the one-sentence summary, the key numbers, and there's no paywall or syndication flag. See examples/agent-conversation.md for a full worked round-trip.
Payments
First 10 parses per device are free. No account, no API key, no human required.
After that: $0.01 per parse (flat, no subscriptions) via x402 — an open micropayment protocol. Your agent's wallet signs the payment automatically. To give your agent a Coinbase wallet, run
npx @coinbase/payments-mcpand connect it alongside NanoParse.
How payments work under the hood
When an agent without free quota calls nanoparse_fetch, the endpoint responds with an HTTP 402 — Payment Required carrying x402 payment instructions. The agent's wallet signs a $0.01 USDC (Base) transfer, the facilitator settles it, and the parse proceeds. The hosted endpoint handles the entire challenge → settlement flow — the agent never needs a private key on your machine.
Pricing vs Firecrawl
NanoParse | Firecrawl Hobby | |
Billing | $0.01 per parse, flat | $16/mo subscription, credits expire monthly |
5,000 parses | $50.00 | $16 base — but ~$145 with JSON/extraction modes (9× credit burn) |
Account / API key | None | Required |
Agent can pay directly (x402) | Yes | No |
Full math, including when Firecrawl's base rate wins at scale: examples/costs.md.
Example
You: Fetch https://arxiv.org/abs/2401.14295 and summarize the argument.
Agent: [calls nanoparse_fetch(url)]
→ clean Markdown + Litmus (academic, authority 0.85, 4-min read,
no date found — verify, no paywall)
→ summarizes from primary content, cites authority and freshnessRepository note
The nanoparse-mcp npm package and the local stdio client in src/ were the
first-generation integration, which held a wallet private key
(NANOPARSE_WALLET_KEY) and signed x402 payments on your machine. Both are
retired: the hosted endpoint above now handles the full payment flow
itself, so no local client or wallet key is needed. The retired client code
is preserved in archive/v1-local-client/ for
history, and the npm package is deprecated. Use the hosted endpoint — it
is the current, supported integration.
Docs
Quick start & integration: nanoparse.app/quickstart
Compare & pricing: nanoparse.app/compare
API + MCP endpoint: nanoparse.app
MIT licensed.
Available Tools
1 toolnanoparse_fetchAInspect
Fetch a web page and return clean, structured Markdown with metadata. First 50 parses free per IP. After that, pay $0.0175 USDC on Base via x402. No account or API key required.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to fetch and convert to Markdown | |
| debug | No | If true, include extraction diagnostics in the response (contentSelector, metadataSource, cleaning stats, timing) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description discloses important behavioral traits: first 50 parses free per IP, then payment via USDC on Base through x402, and no account/API key required. This goes beyond the schema and provides useful operational context, though it omits details like error handling or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose, and includes necessary pricing/authentication details without unnecessary elaboration. Every sentence contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with only 2 parameters and no output schema. The description covers the return format (Markdown with metadata), the free tier, payment cost, and lack of API key requirement, which is complete for the tool's complexity. The debug parameter is documented in the schema, so no gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Both parameters (url, debug) have descriptions in the schema, covering 100% of the parameters. The description itself does not add specific parameter semantics but complements with info about the output format. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a web page and returns clean, structured Markdown with metadata. It provides a specific verb ('Fetch'), resource ('web page'), and output ('Markdown with metadata'), which is sufficient even without sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for converting web pages to Markdown, and includes practical context about free tier and payment. However, it does not explicitly describe when to use it over alternatives, though no siblings exist. Clear context without exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v1.0.6- First observed
nanoparse_fetch
TDQS
Only one tool exists, so there is no possibility of confusion or overlap. The single tool has a clearly defined purpose of fetching and converting web pages to markdown.
The tool name follows a consistent verb_noun pattern: 'nanoparse_fetch'. This is clear and predictable, though with only one tool, consistency is trivial.
Having only one tool feels thin for a server, even for a focused purpose. While the tool covers a specific function, agents might expect additional related operations or options.
For the stated purpose of fetching a web page and returning structured markdown with metadata, the single tool provides complete coverage. No other operations are necessary to fulfill the server's promise.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Monetize any MCP server: x402 paywall, pay-per-call billing in USDC on Base, agent marketplace.
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15 paid AI agent primitives via x402 (USDC on Base). Pay-per-call MCP server.
Agent-commerce MCP server for x402/USDC payments and affiliate splits on Base.
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