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"]
  }
}

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.

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 @codeblue/prime 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_style_guide | Query style guide rules | | 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 |

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