Gemini Code Intel

Gemini Code Intel is a Model Context Protocol (MCP) plugin that enables semantic code search for AI assistants. It provides deep context from an entire codebase by indexing it into a local vector database, allowing efficient and cost-effective retrieval of relevant code snippets.

Modules

Core (@gemini/gemini-code-intel-core)

The core indexing engine responsible for semantic search and codebase analysis.

• Key Features:
  • Multi-language support (TypeScript, JavaScript, Python, Java, C++, and more).
  • Hybrid search combining BM25 and vector search for improved accuracy.
  • AST-based intelligent code chunking to preserve syntax and context.
  • Incremental synchronization using Merkle trees for efficient re-indexing.
• Configuration:
  • ContextConfig: Configures embedding provider, vector database instance, splitting strategy, and file patterns.
• API Reference:
  • indexCodebase: Indexes an entire directory.
  • reindexByChange: Incrementally updates the index based on file changes.
  • semanticSearch: Performs semantic queries against the indexed code.
  • clearIndex: Removes existing index data.

MCP (@gemini/gemini-code-intel-mcp)

Integrates the core engine with the Model Context Protocol for use with AI agents and clients.

• Prerequisites: Node.js (20+), Gemini API key.
• Environment Variables:
  • GEMINI_API_KEY: Required API key for embeddings.
  • EMBEDDING_MODEL: Optional model specification (default: gemini-embedding-001).
  • DB_URI: Optional local storage path (default: ~/.gemini-code-intel/db).
  • EMBEDDING_BATCH_SIZE: Optional batch size for performance tuning.
• Available Tools:
  • index_codebase: Index a codebase directory.
  • search_code: Search using natural language queries.
  • clear_index: Reset the search index.
  • get_indexing_status: Retrieve current indexing progress.

Installation

Recommended: Install as Gemini Extension

This is the recommended way to use Gemini Code Intel as it automatically configures the MCP server and provides an integrated Agent Skill.

Direct Installation (Pre-built)

You can install the extension directly from the repository. The build artifacts (dist folders) are included, so you don't need to manually build the TypeScript code. However, you must still install dependencies because this extension relies on native system bindings (like tree-sitter and vectordb).

1. Install the extension using the CLI:

   gemini extensions install https://github.com/akh64bit/semantic-code-intelligence

2. Navigate to the installed extension directory (usually ~/.gemini/extensions/semantic-code-intelligence or similar).
3. Install the dependencies to fetch the required native binaries for your OS:

   pnpm install

Manual Installation (From Source)

If you are working from the source code, follow these steps:

1. Build the Project:

   pnpm install
   pnpm build

2. Link the Extension:

   gemini extensions link .

Usage

Once installed, the Gemini CLI will automatically have access to the code-intel skill. You can interact with it naturally:

• "Index the current directory"
• "Search the codebase for the JWT validation logic"
• "What is the indexing status for /path/to/project?"

Manual MCP Setup (Optional)

If you prefer to configure the MCP server manually in your settings.json:

1. Build the project as shown above.
2. Add the following to your mcpServers section:

"gemini-code-intel": {
  "command": "node",
  "args": ["/path/to/gemini-code-intel/packages/mcp/dist/index.js"],
  "env": {
    "GEMINI_API_KEY": "your-api-key",
    "DB_URI": "~/.gemini-code-intel/db"
  }
}

Technical Overview

• Storage: Uses high-performance, serverless embedded vector storage.
• Embeddings: Optimized for Google's Gemini embedding models.
• Languages: Supports TypeScript, JavaScript, Python, Java, C++, C#, Go, Rust, PHP, Ruby, Swift, Kotlin, Scala, and Markdown.