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rasterly-mcp

For developers building autonomous agents, the biggest bottleneck is often the unpredictable nature of the modern web. rasterly-mcp solves this by providing a standardized Model Context Protocol interface for high-fidelity web interaction. Unlike simple scraping libraries, this tool enables agents to 'see' and 'understand' URLs through automated screenshots, PDF generation, and clean Markdown extraction. It handles the heavy lifting of asynchronous web automation, including waiting for DOM stability, handling animated banners, and exporting media as GIFs or MP4s. Integration is straightforward, offering a cost-effective alternative to services like Urlbox or ScreenshotOne. A standout feature for agentic workflows is the support for per-call USDC payments on Base, allowing for keyless, scalable execution without the friction of traditional API key management. Whether you are building a research agent that needs structured JSON data or a monitoring bot that requires visual verification, this MCP provides the robust browser context required for reliable decision-making.

Open ecosystemWeb ScrapingWeb Scraping
View GitHub
01 / SERVER DETAIL

Server detail

For developers building autonomous agents, the biggest bottleneck is often the unpredictable nature of the modern web. rasterly-mcp solves this by providing a standardized Model Context Protocol interface for high-fidelity web interaction. Unlike simple scraping libraries, this tool enables agents to 'see' and 'understand' URLs through automated screenshots, PDF generation, and clean Markdown extraction. It handles the heavy lifting of asynchronous web automation, including waiting for DOM stability, handling animated banners, and exporting media as GIFs or MP4s. Integration is straightforward, offering a cost-effective alternative to services like Urlbox or ScreenshotOne. A standout feature for agentic workflows is the support for per-call USDC payments on Base, allowing for keyless, scalable execution without the friction of traditional API key management. Whether you are building a research agent that needs structured JSON data or a monitoring bot that requires visual verification, this MCP provides the robust browser context required for reliable decision-making.

An MCP server that exposes rasterly-mcp capabilities to MCP-compatible AI clients.

CapabilitiesWeb ScrapingMCPOpen source
ProtocolModel Context Protocol
Developer ecosystemOpen ecosystem
LicenseMIT / check source
SourceGitHub
COLLECTIONS

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02 / TOOL TESTING

Tool testing

AVAILABLE TOOL

rasterly-mcp

Callable

Call the MCP capabilities provided by rasterly-mcp and return a structured result.

PARAMETERS
inputPass arguments according to the server tool schema.
Tip: the tool may truncate responses. Use pagination parameters such as start_index to read long content in chunks.
03 / SERVICE CONFIGURATION

Connection modes

Remote
{
  "mcpServers": {
    "rasterly-mcp": {
      "url": "Generated by the provider after deployment"
    }
  }
}

The Remote endpoint is generated by the provider after deployment; this page does not fabricate an unusable endpoint.

03 / WORKFLOW

How to use

  1. 01
    Step 1

    Review server capabilities and permission scope.

  2. 02
    Step 2

    Copy the install command or JSON configuration.

  3. 03
    Step 3

    Run a small connection test in your client.

  4. 04
    Step 4

    Adopt it long term only after reviewing access and maintenance.

04 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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