Codebase understanding for AI coding agents — symbol graph, blast radius, semantic search.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-luuuc-sense": {
"command": "<see-readme>",
"args": []
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
Codebase understanding for AI coding agents — symbol graph, blast radius, semantic search.
No automated test available for this server. Check the GitHub README for setup instructions.
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
No package registry to scan.
This server is missing a description. Tools and install config are also missing.If you've used it, help the community.
Add informationBe the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in search
Web and local search using Brave Search API
Production ready MCP server with real-time search, extract, map & crawl.
mini cli search engine for your docs, knowledge bases, meeting notes, whatever. Tracking current sota approaches while being all local
MCP server for Firecrawl — search, scrape, and interact with the web. Supports both cloud and self-hosted instances. Features include web search, scraping, page interaction, batch processing, and LLM-powered content analysis.
MCP Security Weekly
Get CVE alerts and security updates for io.github.luuuc/sense and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
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.
Measured across 7 real-world codebases (Discourse, Flask, Next.js, Axum, Gin, Javalin, and a private e-commerce repo).
Full methodology and raw data: bench/. Head-to-head leaderboard against Serena, Probe, GitNexus, code-search MCPs: docs/bench-leaderboard.svg.
| Metric | Claude Code (Opus 4.6) | Claude Code (Opus 4.6) + Sense | Change |
|---|---|---|---|
| Tool calls per task | 19 | 10 | -47% |
| Tokens per task | 228K | 156K | -32% |
| Cost per task | $0.42 | $0.31 | -26% |
| Session time | 91s | 73s | -19% |
| Score per 100K tokens | 0.19 | 0.30 | +64% |
| Score per minute | 0.28 | 0.38 | +37% |
Same correctness, dramatically less work. Sense doesn't make the model smarter. It gives the model structural understanding so it stops wasting effort.
Tasks that require understanding code relationships, not just reading text, are where Sense pulls ahead.
| Task type | Baseline | + Sense | Why |
|---|---|---|---|
| Blast radius | 0.17 | 0.25 | Pre-computed dependency graph vs. manual grep chains |
| Find callers | 0.27 | 0.33 | Graph lookup vs. reading dozens of files |
| Dead code | 0.00 | 0.05 | Baseline can't do this at all |
| Semantic search | 0.36 | 0.38 | Two-stage retrieval (bi-encoder + cross-encoder) with text fallback |
Sense is structural understanding, not a general search engine. For tasks that are fundamentally text-grep (find a log message, locate a string literal), plain grep is the right tool and Sense adds nothing. Search text fallback (ripgrep) bridges some of this gap, but it's a fallback, not a replacement.
curl -fsSL https://luuuc.github.io/sense/install.sh | sh
Or download the binary for your OS from the latest release, unzip, and move sense somewhere on your PATH.
go install github.com/luuuc/sense/cmd/sense@latest
cd /path/to/project && sense scan
Parses your code with tree-sitter, extracts symbols and relationships, embeds everything with a bundled ONNX model