MCP 服务器

Codebase Context

Provides AI assistants with real-time visibility into your codebase's internal libraries, team patterns, naming conventions, and usage frequencies to generate code that matches your team's actual practices.

README

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.