blowsh-mcp is a Model Context Protocol (MCP) server that exposes the power of Browsh—a fully JavaScript-capable terminal browser—to any AI Agent, IDE agent, or MCP client. This project allows your…
blowsh-mcp is an MCP project maintained in the mokhtarabadi/blowsh-mcp repository. The maintainer describes its purpose as “blowsh-mcp is a Model Context Protocol (MCP) server that exposes the power of Browsh—a fully JavaScript-capable terminal browser—to any AI Agent, IDE agent, or MCP…”. Read the project documentation for the complete scope.
The project provides a local client configuration using node. Follow the repository’s build and installation steps before copying it, and replace example paths with the paths on your own computer.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| fetch_web | { url, type: "plain"\ |
| search_web | { query: string, max_results?: number, page?: number, enrich?: boolean, query_variants?: string[] (max2), intent?: "auto"\ |
| crawl_web | { url: string, mode?: "full"\ |
| extract_links | { url: string, limit?: number } |
| fetch_web_batch | { urls: string[], type: "plain"\ |
{
"mcpServers": {
"blowsh": {
"command": "node",
"args": [
"dist/server.js"
],
"env": {}
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
Retrieve public web pages as readable Markdown with the reference Fetch MCP server, including pagination for longer responses.
Use Firecrawl MCP to retrieve website content and extract information for research through Firecrawl’s API-backed web tools.
The original Chromium automation reference server — simple, screenshot-driven browser control.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Automates browsers and APIs, enabling Large Language Models (LLMs) to interact with web pages for tasks like screenshot capture and JavaScript
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.