Self-hosted web search for LLM agents: search -> fetch -> rank pipeline with semantic caching
Run one of the commands below, then add the client config underneath.
Pythonuvx webfetch-llm
Paste into Claude Desktop, Cursor (mcp.json), VS Code or any MCP client, then restart the client.
mcpServers{
"mcpServers": {
"webfetch-2": {
"command": "uvx",
"args": [
"webfetch-llm"
]
}
}
}
webfetch is listed in the Search category of the MCPNav directory. It is distributed as Python and can be loaded by any client that speaks the Model Context Protocol.
Typical uses include giving your assistant scoped access to the corresponding service so it can answer questions and take actions with real data instead of guessing. Always review what a server can access before you enable it — see our MCP security guide.
webfetch is an MCP server by firish. Self-hosted web search for LLM agents: search -> fetch -> rank pipeline with semantic caching
Install it with: uvx webfetch-llm. Then add the JSON config to your client's MCP settings and restart the client.
The MCP server itself is free to install. The source is public on https://github.com/firish/webfetch. Any third-party API it calls (such as a search or maps API) may require its own key and billing.
Any MCP-compatible client can use it, including Claude Desktop, Cursor, VS Code, Windsurf and custom agents.