Strategies & Examples

Get the most out of Ask Gemini MCP by leveraging its massive 1M+ token context window. Here are proven strategies and workflows.

🎯 The @ File Syntax

When asking Gemini to analyze your codebase, you can explicitly target files, directories, or patterns using the @ symbol inside your prompt.

Single & Multiple Files

Ask Gemini to explain @index.js and @src/auth.ts

Entire Directories

Ask Gemini to summarize the architecture in @src/

What is the purpose of this project? Read @. (current directory)

Wildcards & Globs

review @routes/**/*.js for OWASP vulnerabilities

Tip: Including package.json (@package.json @src/) helps Gemini understand your dependencies before analyzing your code!

---

🛠️ Real-World Workflows

1. The "Second Opinion" Code Review

Don't just rely on one AI model. When you're about to make a major architectural change or submit a Pull Request, ask Gemini for a secondary review.

"Ask Gemini to review the staged changes in @feature/new-api/*.js for security issues, performance concerns, and missing error handling."

2. Massive Codebase Analysis

Claude is excellent at writing code, but its context window gets expensive and full quickly. Offload heavy reading to Gemini's 1 Million+ token context window.

# Architecture overview
"Ask Gemini to give me a high-level overview of how the frontend and backend connect based on @package.json @src/index.js @client/App.jsx"

# Dependency Analysis
"@package.json @package-lock.json are there any security vulnerabilities or outdated packages?"

3. Debugging Complex Stack Traces

When your app crashes with a cryptic error, feed the log and the relevant source code to Gemini.

"@error.log @src/api.js I'm getting 500 errors on the /user endpoint after our recent deployment. What caused this crash?"

(Always provide the error log alongside the source file so Gemini has full context).

4. Code Generation & Sandbox Execution

If you want Gemini to write a script and actually verify it runs, ask it to use its execution sandbox.

"Ask Gemini in sandbox mode to create a Python data validation script and run it against a mock dataset."

---

🛟 Best Practices

1. Start Broad, Then Narrow: "First analyze the architecture" → "Now focus on the authentication module" → "Write tests for the auth module."
2. Be Specific About Intent: Don't say "analyze this code". Say "identify performance bottlenecks and suggest optimizations targeting junior developers."
3. Model Selection: The tool defaults to Gemini 3.1 Pro. If you just need a quick syntax check on a single file, you can explicitly ask for Flash to get a faster response: "Use Gemini Flash to check my CSS in @styles.css".