MCP Codebase Mentor

MCP Codebase Mentor

An MCP (Model Context Protocol) server that acts as an AI mentor for any codebase using dual-layer indexing.

Features

• Universal language support - AI handles all programming languages
• Complete file coverage - Indexes code, tests, configs, and docs
• Smart filtering - Respects .gitignore and applies sensible defaults
• Semantic search - Vector-based code search using LlamaIndex
• Tutorial generation - Creates structured learning guides with architecture diagrams

Installation

# Clone the repository
git clone <repository-url>
cd mcp-codebase

# Install dependencies
npm install

# Build the project
npm run build

Usage with Cursor/Claude

Add to your MCP configuration:

{
  "mcpServers": {
    "codebase-mentor": {
      "command": "node",
      "args": ["/path/to/mcp-codebase/dist/index.js"]
    }
  }
}

Available Tools

init_codebase

Initialize and index a codebase for AI mentoring.

init_codebase(rootPath: "/path/to/your/project")

This will:

1. Crawl the directory structure (respecting .gitignore)
2. Analyze each file with AI to extract summaries, imports, and exports
3. Build a manifest with file metadata and dependency graph
4. Create a vector index for semantic search

Output files:

• .mcp_manifest.json - File metadata and dependency graph
• .mcp_index/ - Vector index for semantic search

generate_tutorial

Generate a comprehensive "Zero to Hero" tutorial for a codebase.

generate_tutorial(rootPath: "/path/to/your/project", focusTopic?: "authentication")

Creates:

• Project overview and architecture
• Mermaid.js dependency diagrams
• Structured learning path (chapters)
• Key insights and patterns

search_codebase

Perform semantic search across a codebase.

search_codebase(rootPath: "/path/to/your/project", query: "how is authentication handled?")

Returns relevant code snippets with:

• File paths and line numbers
• Relevance scores
• File context and summaries

Project Structure

mcp-codebase/
├── src/
│   ├── index.ts                    # MCP server entry point
│   ├── tools/
│   │   ├── init.ts                 # init_codebase implementation
│   │   ├── tutorial.ts             # generate_tutorial implementation
│   │   └── search.ts               # search_codebase implementation
│   ├── core/
│   │   ├── crawler.ts              # File system walker (.gitignore aware)
│   │   ├── analyzer.ts             # LLM-based file analysis
│   │   ├── manifest.ts             # Manifest CRUD operations
│   │   └── vectorIndex.ts          # LlamaIndex integration
│   ├── utils/
│   │   ├── fileFilter.ts           # Smart file filtering logic
│   │   ├── languageDetect.ts       # Language/file type detection
│   │   ├── progress.ts             # Progress reporter
│   │   └── git.ts                  # Git metadata extraction
│   ├── prompts/
│   │   ├── analyze.ts              # Universal file analysis prompt
│   │   └── curriculum.ts           # Tutorial generation prompt
│   └── types/
│       ├── manifest.ts             # Manifest type definitions
│       └── mcp.ts                  # MCP tool interfaces
├── package.json
├── tsconfig.json
└── README.md

Development

# Type checking
npm run typecheck

# Development mode with auto-reload
npm run dev

# Build for production
npm run build

Performance Expectations

For a typical repository:

• 500 files: ~10-15 minutes (mostly AI analysis)
• 1000 files: ~20-30 minutes
• 5000 files: ~2 hours

Initialization is a one-time operation. Subsequent queries use the cached index.

Storage

For a 500-file repository (~50MB source):

• Manifest: ~100-200 KB
• Vector Index: ~5-10 MB
• Total overhead: ~20% of source size

Limitations

1. LLM Dependency: Initialization requires an MCP host with sampling capability
2. No Incremental Updates: Re-run init_codebase when files change significantly
3. Binary Files: Skipped (images, PDFs, executables)
4. Very Large Files: May hit LLM context limits (>100K tokens)

License

MIT