Sense MCP server

Sense ⠎⠑⠝⠎⠑

An MCP server for codebase context, built for AI coding agents.

Your AI agent reads 19 files to answer "who calls this function?" because it has the text of your codebase but never the map. It re-derives structure on every task, burns tokens chasing grep chains, and hallucinates dependencies that don't exist. Sense indexes your repo once and serves a symbol graph plus semantic code search over MCP. Claude Code, Cursor, Codex CLI, and any MCP client finish the same task in 10 tool calls instead of 19, on 156K tokens instead of 228K, with the same correctness (bench/).

One binary, one local index, four tools for your AI. No SaaS account, no API key, no cloud dependency.

Sense sits on your machine, has no learning curve, and isn't for you. It's for your AI.

New here? Start with the Guide for a map to every doc, by who you are.

What Sense believes

A codebase is structure, not just text. The graph of what calls what, what breaks what, and how the team writes is real, knowable, and worth holding onto. Sense keeps it on your machine and hands it to your AI.

The rest follows from that. Local, because your code is yours. Read-only, because understanding shouldn't require permission to change things. Four tools, because your AI needs a few that work, not a hundred to pick from.

These aren't features. They're the reason Sense looks the way it does.

What Your AI Gets

Four tools for your AI. A full CLI for you. No sprawl.

Your AI stops reading 30 files to answer "who calls this?" It stops hallucinating dependencies. It stops writing code that's correct but doesn't match how your team writes code.

Convention detection

Of the four tools, convention detection is the one nobody else does well. AI tools don't just struggle with structure. They struggle with style. They write correct code that doesn't follow how YOUR codebase writes code.

Sense detects patterns from your actual source code: key types and their declarations, framework idioms (Rails associations, Go interfaces, Django models), architectural layers, and naming conventions. Your AI follows these patterns because it sees them, not because it was told about them. Convention detection isn't a feature. It's the thing that makes AI-written code feel like it belongs.

What changes

Shortened. The full README is on GitHub.