A friendly MCP server for web research - fetch pages and search the web
Connect MCP Research Friend to Claude, Cursor or any other MCP client and it stops being a tab you switch to. A friendly MCP server for web research - fetch pages and search the web. The mcp research friend mcp server is what makes that connection.
A friendly helper for AI assistants that need to look things up on the web and manage a local research stash.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
friendly_web_fetch — Fetches a web page and returns its content. By default, returns markdown with links preserved — ideal for LLMs. Usesfriendly_search — The friendly_search tool exposed by this serverfriendly_web_extract — Extracts content from a URL. Auto-detects whether the URL points to a PDF or a web page and handles each appropriatelyfriendly_web_ask — Fetches a URL (PDF or web page) and has an LLM answer questions about it. Auto-detects content type. The document is processed in a separate contextstash_open_inbox — Open the stash inbox folder in your file manager for easier drag-and-dropstash_process_inbox — Process files in inbox/, classify them into topics, extract text, and store results. For long documents, classification uses sampled sectionsreindex_stash — Regenerate summaries, re-allocate topics, and update store metadata for stashed documents. If ids is omitted or empty, all documents are reindexedstash_list — The stash_list tool exposed by this serverstash_search — Search filenames and content across the stash. All search terms must be present (AND logic). Filename matches are listed first. Use quotes for exactstash_extract — Extract content from a stashed document for reading. Use line numbers from stash_search results to jump directly to matchesstash_ask — Have an LLM answer questions about a stashed document. The document is processed in a separate context, keeping your main conversation compactInstallation goes through your MCP client rather than a global install: point it at playwright on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. MCP Research Friend's toolset — friendly_web_fetch, friendly_search, friendly_web_extract and 8 more — is a fair guide to whether it matches your workflow. It is maintained by permacommons; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against MCP Research Friend's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| friendly_web_fetch | Fetches a web page and returns its content. By default, returns markdown with links preserved — ideal for LLMs. Uses [Readability](https://github.com/mozilla/readability) to extract the main content (stripping navigation |
| friendly_search | The friendly_search tool exposed by this server. |
| friendly_web_extract | Extracts content from a URL. Auto-detects whether the URL points to a PDF or a web page and handles each appropriately. |
| friendly_web_ask | Fetches a URL (PDF or web page) and has an LLM answer questions about it. Auto-detects content type. The document is processed in a separate context, keeping your main conversation compact. |
| stash_open_inbox | Open the stash inbox folder in your file manager for easier drag-and-drop. |
| stash_process_inbox | Process files in inbox/, classify them into topics, extract text, and store results. For long documents, classification uses sampled sections (start/middle/end plus a few random chunks) to improve topic accuracy. |
| reindex_stash | Regenerate summaries, re-allocate topics, and update store metadata for stashed documents. If ids is omitted or empty, all documents are reindexed. |
| stash_list | The stash_list tool exposed by this server. |
| stash_search | Search filenames and content across the stash. All search terms must be present (AND logic). Filename matches are listed first. Use quotes for exact phrases. |
| stash_extract | Extract content from a stashed document for reading. Use line numbers from stash_search results to jump directly to matches. |
| stash_ask | Have an LLM answer questions about a stashed document. The document is processed in a separate context, keeping your main conversation compact. |
{
"mcpServers": {
"research-friend": {
"command": "npx",
"args": ["-y", "playwright"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.