codebase-context

AI coding agents don't know your codebase. This MCP fixes that.

Your team has internal libraries, naming conventions, and patterns that external AI models have never seen. This MCP server gives AI assistants real-time visibility into your codebase: which libraries your team actually uses, how often, and where to find canonical examples.

Quick Start

Add this to your MCP client config (Claude Desktop, VS Code, Cursor, etc.).

"mcpServers": {
  "codebase-context": {
    "command": "npx",
    "args": ["codebase-context", "/path/to/your/project"]
  }
}

If your environment prompts on first run, use npx --yes ... (or npx -y ...) to auto-confirm.

What You Get

• Internal library discovery → @mycompany/ui-toolkit: 847 uses vs primeng: 3 uses
• Pattern frequencies → inject(): 97%, constructor(): 3%
• Pattern momentum → Signals: Rising (last used 2 days ago) vs RxJS: Declining (180+ days)
• Golden file examples → Real implementations showing all patterns together
• Testing conventions → Jest: 74%, Playwright: 6%
• Framework patterns → Angular signals, standalone components, etc.
• Circular dependency detection → Find toxic import cycles between files

How It Works

When generating code, the agent checks your patterns first:

Without MCP	With MCP
Uses constructor(private svc: Service)	Uses inject() (97% team adoption)
Suggests primeng/button directly	Uses @mycompany/ui-toolkit wrapper
Generic Jest setup	Your team's actual test utilities

Tip: Auto-invoke in your rules

Add this to your .cursorrules, CLAUDE.md, or AGENTS.md:

When generating or reviewing code, use codebase-context tools to check team patterns first.

Now the agent checks patterns automatically instead of waiting for you to ask.

Tools

Tool	Purpose
search_codebase	Semantic + keyword hybrid search
get_component_usage	Find where a library/component is used
get_team_patterns	Pattern frequencies + canonical examples
get_codebase_metadata	Project structure overview
get_indexing_status	Indexing progress + last stats
get_style_guide	Query style guide rules
detect_circular_dependencies	Find import cycles between files
refresh_index	Re-index the codebase

Configuration

Variable	Default	Description
EMBEDDING_PROVIDER	transformers	openai (fast, cloud) or transformers (local, private)
OPENAI_API_KEY	-	Required if provider is openai
CODEBASE_ROOT	-	Project root to index (CLI arg takes precedence)
CODEBASE_CONTEXT_DEBUG	-	Set to 1 to enable verbose logging (startup messages, analyzer registration)

Performance Note

This tool runs locally on your machine using your hardware.

• Initial Indexing: The first run works hard. It may take several minutes (e.g., ~2-5 mins for 30k files) to compute embeddings for your entire codebase.
• Caching: Subsequent queries are instant (milliseconds).
• Updates: Currently, refresh_index re-scans the codebase. True incremental indexing (processing only changed files) is on the roadmap.

Links

• 📄 Motivation — Why this exists, research, learnings
• 📋 Changelog — Version history
• 🤝 Contributing — How to add analyzers

License

MIT