Arjunkmrm Fetch
Let your agent fetch live web pages and pull only the HTML you need via CSS selectors—ideal for competitor pages, docs snippets, and validation research without manual copy-paste.
Overview
ai.smithery/arjunkmrm-fetch is a MCP server for the Idea phase that fetches URLs and returns page content filtered by CSS selectors.
What is this MCP server?
- Fetches web pages over HTTP through Smithery-hosted remote MCP (v1.0.0)
- Extracts content with CSS selectors so agents return targeted fragments, not whole pages
- Bearer-authenticated streamable-http remote—minimal local install surface
- Supports idea-phase competitor and audience research from live URLs
- Server version 1.0.0; schema 2025-09-16
- GitHub source repository: github.com/arjunkmrm/fetch
- Hosted at server.smithery.ai/@arjunkmrm/fetch/mcp
What problem does it solve?
Manual copy-paste from competitor and docs pages breaks flow and agents guess stale page content when they cannot retrieve the live HTML.
Who is it for?
Solo builders doing fast competitive research, landing teardowns, and public-docs extraction inside MCP-enabled agents.
Skip if: Heavy authenticated scraping, full SPAs that require a headless browser, or high-volume crawling pipelines—use dedicated browser or ETL tooling.
What do I get? / Deliverables
After registration, your agent can pull selector-scoped snippets from real URLs while you research and validate what to build.
- Live HTML fetch results in agent conversations
- Selector-scoped text or markup for research briefs
- Remote MCP v1.0.0 endpoint on Smithery
Recommended MCP Servers
Journey fit
Early journey research depends on reading what is actually on the web today; fetch-plus-select keeps that loop inside the agent. Research is where you scrape landing copy, pricing tables, and competitor positioning before you commit to a build scope.
How it compares
CSS-targeted HTTP fetch MCP, not a headless Chrome skill or managed data pipeline.
Common Questions / FAQ
Who is ai.smithery/arjunkmrm-fetch for?
Builders and researchers who want agents to read live web pages and return specific sections via CSS selectors during early product discovery.
When should I use ai.smithery/arjunkmrm-fetch?
Use it when validating ideas, comparing competitor pages, or quoting public documentation without leaving your agent chat.
How do I add ai.smithery/arjunkmrm-fetch to my agent?
Configure the Smithery remote MCP URL for @arjunkmrm/fetch and pass Bearer smithery_api_key in the Authorization header per server.schema.json.