| name | sense |
| description | Local-only MCP server that gives AI coding agents structural understanding of your codebase — symbol graph, blast radius, semantic search, and auto-detected project conventions. Reduces tool calls and tokens for any task that depends on understanding code relationships rather than reading text. |
| license | Other |
Sense: Codebase Understanding for AI Coding Agents
Sense is an MCP server that gives AI agents the structural understanding of a codebase that a senior engineer carries in their head. It runs as one Go binary with a local SQLite index — no API keys, no SaaS, no cloud calls.
When to Use This Skill
Use Sense whenever the task depends on understanding code relationships, not just reading text:
- "Who calls this function?" / "What does this function call?"
- "What breaks if I change this signature?" (blast radius)
- "Find the implementation of the X pattern" (semantic search)
- "What conventions does this project follow?" (e.g., naming, Rails idioms, Go interface patterns)
- "Find dead code"
- "Show me all routes / handlers / models / controllers"
Do NOT use Sense for:
- Locating a string literal or log message — plain grep is faster.
- Editing code — Sense is read-only by design.
Setup (one-time per project)
curl -fsSL https://luuuc.github.io/sense/install.sh | sh
sense scan
sense setup
After sense setup, your AI tool (Claude Code, Cursor, Codex CLI) has Sense's four tools available via MCP.
Tools
| Tool | Capability |
|---|
sense_graph | Symbol relationships — callers, callees, inheritance, tests, dead code |
sense_search | Hybrid semantic + keyword search with text fallback |
sense_blast | Blast radius, affected code, affected tests, risk score |
sense_conventions | Auto-detected project conventions (Rails, React, Django, Go, naming) |
Plus a generated .sense/summary.md at session-start: top namespaces, hub symbols, entry points, conventions — so the AI is oriented before its first tool call.
How to Call (MCP)
After sense setup, the tools are exposed automatically. Prefer them over grep/glob for any structural question:
- "Who calls X?" →
sense_graph symbol="X" direction=callers
- "What would break if I change Y?" →
sense_blast symbol="Y"
- "Find code that does Z" →
sense_search query="natural language description"
- "How does this project define services?" →
sense_conventions
Performance
| Operation | p50 / p95 |
|---|
| Graph query | 0.2 ms / 3 ms |
| Blast radius | 0.1 ms / 10 ms |
| Conventions | 16 ms / 16 ms |
| Cold start | 48 ms |
| Full scan | 4.9 s |
| Incremental scan | 2.3 s |
Measured on Sense's own codebase (382 files, 4,032 symbols).
Why Sense Helps Agents
Across 7 real-world codebases (Discourse, Flask, Next.js, Axum, Gin, Javalin, and a private e-commerce repo), Claude Code with Sense used 47% fewer tool calls and 32% fewer tokens per task at the same correctness as baseline Claude Code. Against 7 other code-intelligence MCPs (Serena, Probe, GitNexus, GrepAI, ChunkHound, codebase-memory-mcp, TokenSave, Roam), Sense ranks #1 on the head-to-head leaderboard.
Sense doesn't make the model smarter. It gives the model structural understanding so it stops wasting effort.
Language Support
Full tier (framework inference): Ruby + Rails (associations, callbacks, routes, Stimulus, Turbo), TypeScript/JavaScript + React (JSX), Python + Django (models, URLs) + FastAPI (routes, Depends), Go, Rust, ERB.
Standard tier (symbols + calls + inheritance + imports + blast + search): Java, Kotlin, C#, C++, C, PHP, Scala.
Where to Learn More
