Generate a map of your codebaseto help AI Agents understand your architecture, coding conventions and patterns. Discoverable with Semantic Search

codebase-context

Stop paying for AI agents to explore your codebase. codebase-context pre-maps the architecture, conventions, and team memory so they don't have to.

You're tired of AI agents writing code that 'just works' but fits like a square peg in a round hole - not your conventions, not your architecture, not your repo. Even with well-curated instructions. You correct the agent, it doesn't remember. Next session, same mistakes.

This MCP gives agents just enough context so they match how your team codes, know why, and remember every correction.

Here's what codebase-context does:

Finds the right context - Search that doesn't just return code. Each result comes back with analyzed and quantified coding patterns and conventions, related team memories, file relationships, and quality indicators. It knows whether you're looking for a specific file, a concept, or how things wire together - and filters out the noise (test files, configs, old utilities) before the agent sees them. The agent gets curated context, not raw hits.

Knows your conventions - Detected from your code and git history, not only from rules you wrote. Seeks team consensus and direction by adoption percentages and trends (rising/declining), golden files. Tells the difference between code that's common and code that's current - what patterns the team is moving toward and what's being left behind.

Remembers across sessions - Decisions, failures, workarounds that look wrong but exist for a reason - the battle scars that aren't in the comments. Recorded once, surfaced automatically so the agent doesn't "clean up" something you spent a week getting right. Conventional git commits (refactor:, migrate:, fix:) auto-