Cursor Agent vs Gemini CLI vs Codex CLI

Three coding agents now dominate the terminal: Cursor Agent, Gemini CLI, and Codex CLI. All three shipped major updates in Q1 2026, and all three have radically different philosophies about how an agent should browse the web. r/PiCodingAgent and r/ClaudeCode are full of "which one should I pick?" threads. Here is the honest breakdown after shipping with all three for a month.

The Short Answer

• Cursor Agent: best for IDE-centric workflows and multi-file refactors. MCP tool support is strong.
• Gemini CLI: best for long-context tasks that need fresh web data. Native search is middling; bring your own search tool.
• Codex CLI: best for fast, sandboxed execution of discrete coding tasks. Weakest web story out of the box.

Web Search: The Differentiator

The biggest gap between these three is how they handle web search. All three have a built-in, and all three are underwhelming.

• Cursor Agent: ships a lightweight indexed search, tuned for doc-style content. Great for "find the React 20 hooks API docs," weak for "what are people saying about library X on Reddit."
• Gemini CLI: uses Google's grounding API. High quality but costs extra and burns tokens fast. No structured SERP fields.
• Codex CLI: built-in webfetch is literal URL fetch with no search. You need a search tool on top.

The fix is the same across all three: add Scavio as an MCP tool. One config, fresh data in every agent.

MCP Setup for Each

Cursor Agent

Edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "scavio": {
      "command": "npx",
      "args": ["-y", "@scavio/mcp"],
      "env": { "SCAVIO_API_KEY": "${SCAVIO_API_KEY}" }
    }
  }
}

Gemini CLI

Edit ~/.config/gemini/mcp.json:

{
  "mcpServers": {
    "scavio": {
      "command": "npx",
      "args": ["-y", "@scavio/mcp"],
      "env": { "SCAVIO_API_KEY": "${SCAVIO_API_KEY}" }
    }
  }
}

Codex CLI

Pass a tool config inline or via ~/.codex/tools.json:

codex --tools scavio --mcp @scavio/mcp

Benchmarks

We ran the same 10 tasks (mix of refactor, research, bug-fix, and library-swap tasks) on all three agents with Scavio as the search backend.

• Cursor Agent: 9/10 succeeded; average 4.2 Scavio calls per task.
• Gemini CLI: 8/10 succeeded; average 6.1 Scavio calls (verbose tool use).
• Codex CLI: 7/10 succeeded; average 3.5 Scavio calls (tighter but sometimes skipped research).

Pick One Based on Your Workflow

• Pair-programming inside VSCode-like UI: Cursor Agent.
• Long-running research tasks with fresh data: Gemini CLI.
• Sandboxed, terminal-native coding sprints: Codex CLI.

Whichever you pick, add Scavio as the search layer. Get a free key and configure in under two minutes.