About fetcher
fetcher is an MCP server and API that gives AI agents web retrieval and domain-inspection capabilities. It combines page rendering and extraction with DNS, SSL, and WHOIS checks, with per-call USDC payments via x402 after the free daily tier.
Key features
- 01Render and screenshot web pages
- 02Extract content and parse feeds
- 03Resolve links and inspect headers
- 04Check domains, DNS, SSL, and WHOIS
- 05Pay per call in USDC via x402
Use cases
- 01Capture a page for an agent
- 02Extract readable content from a URL
- 03Inspect DNS or SSL details for a domain
MCP support
It exposes web and domain capabilities to MCP clients through the `/mcp` endpoint. Agents can discover capabilities and call tools through MCP or the HTTP API.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://api.fetcher.cloud/mcpclaude mcp add --transport http cloud-fetcher-fetcher https://api.fetcher.cloud/mcp{"mcpServers":{"cloud-fetcher-fetcher":{"type":"http","url":"https://api.fetcher.cloud/mcp"}}}Open in Cursor{"servers":{"cloud-fetcher-fetcher":{"type":"http","url":"https://api.fetcher.cloud/mcp"}}}Open in VS Code{"mcpServers":{"cloud-fetcher-fetcher":{"type":"http","url":"https://api.fetcher.cloud/mcp"}}}{"mcpServers":{"cloud-fetcher-fetcher":{"serverUrl":"https://api.fetcher.cloud/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
19| Parameter | Type | Required | Description |
|---|---|---|---|
| url | string | Yes | Target URL or hostname. |
| full_page | boolean | No | |
| screenshot | boolean | No |
Details
From the project READMEfetcher.cloud — the web capability layer for AI agents
One MCP server and API that gives an autonomous AI agent the public web. Render and screenshot pages, extract clean content, resolve links, parse feeds, and inspect domains, DNS, SSL, and WHOIS — 18 capabilities, paid per-call in USDC via x402, with a free daily tier.
Whenever an agent needs something from the internet, it uses fetcher.
Why it exists
LLMs are brilliant at reasoning and blind at retrieval. The moment real-world data needs a browser, a payment, or a login, an agent stalls. fetcher is the specialist capability layer it calls — a single interface for everything an agent can't do alone, billed the only way that works for software: per call, in stablecoins, with no account or API key.
How agents use it
- Discover —
GET /capabilitiesreturns every capability with category, pricing, examples, and schemas. Or connect the MCP server at/mcp. - Call — a normal
GETrequest. The first unpaid call past the free tier returns402 Payment Requiredwith machine-readable terms. - Pay — the agent's wallet pays a fraction of a cent in USDC on Base and retries. No human in the loop.
Works with Conway Automaton, Claude, OpenAI Agents, Google ADK, any MCP client, or any agent that can make an HTTP request.
The capabilities
| Category | Capabilities |
|---|---|
| Browser & rendering | render, screenshot |
| Content & extraction | extract, unfurl, feed, oembed, pdf-text, diff |
| Web intelligence | resolve, headers, robots, favicon |
| Domain & DNS | domain, domain/enriched, whois, dns, ssl |
| Utility | qr |
Full catalog with pricing and examples: https://api.fetcher.cloud/capabilities
Machine-readable surfaces
/ (manifest) · /capabilities · /pricing.json · /openapi.json · /mcp ·
/.well-known/ai-plugin.json · llms.txt
Repository layout
worker/ # the Cloudflare Worker (API, payments, MCP) → see worker/README.md
website/ # the landing page (Cloudflare Pages)
test-client/ # a script that pays an endpoint on Base mainnet
conway-skill/ # ready-to-submit Conway Automaton skill
Deploy
See worker/README.md and DEPLOY.md for step-by-step deployment
(Cloudflare Workers + Pages). Payments require Coinbase CDP facilitator keys
set as Worker secrets.
License
MIT — see LICENSE.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.