Codebase understanding for AI coding agents — symbol graph, blast radius, semantic search.
Codebase understanding for AI coding agents — symbol graph, blast radius, semantic search.
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/).
.sense/ index (varies with project size)Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| sense_graph | Symbol relationships, callers, callees, inheritance, tests, dead code |
| sense_search | Hybrid bi-encoder + cross-encoder semantic code search with keyword and text fallback |
| sense_blast | Blast radius, affected code, affected tests, risk score |
| sense_conventions | Detected project conventions from source |
.sense/ index (varies with project size)Read, search and edit files inside allowed directories with the Model Context Protocol reference Filesystem server.
Retrieve version-aware library documentation and code examples with Context7 MCP, using resolve-library-id and query-docs.
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[Read the documentation!](https://phil65.github.io/mcp-server-llmling/)