Open-source Deep Research

Deep Research for Cursor

Let Cursor pull cited external evidence before it edits code or drafts implementation plans.

Install path matches agent workflows

AutoSearch works as an MCP server that Cursor can call from agent sessions. Instead of copying browser research into the editor, your prompt can ask Cursor to gather source-backed context first, then apply the evidence to the files already open.

Better context before code changes

Cursor is strongest when its edits start from good constraints. AutoSearch adds current documentation, issue threads, benchmark posts, and community examples before implementation begins, which reduces guesswork around package behavior, platform limits, and migration edge cases.

Portable beyond one editor

Because AutoSearch is LLM-decoupled and MCP-native, teams can keep the same research behavior while experimenting with different agent hosts. Cursor gets the immediate benefit, and the underlying source workflow remains usable in other MCP-capable environments.

How it fits

AutoSearch plugs into Cursor as a research-capable MCP server. Cursor still owns code navigation, editing, diff review, and final reasoning. AutoSearch is called when the agent needs external evidence from docs, GitHub, arXiv, social channels, or Chinese technical sources. This keeps research close to the code task while preserving a clean boundary: Cursor decides what to do with the evidence, and AutoSearch focuses on collecting cited, source-diverse results.

Try this prompt

Before editing, research how teams structure Vite React apps with MCP clients.
Return cited patterns, risks, and examples from GitHub and docs.