// PROJECT · AI & AGENTS
mcp-gtags-server
Indexed code navigation for AI coding agents — replaces full-tree grep scans with GNU Global (gtags) lookups over the Model Context Protocol. ~100× faster per query on kernel-scale codebases, with radically less context noise.
- Python
- MCP
- GNU Global
- ctags
Every AI coding agent answers “where is this function defined?” the same way: grep the
entire tree. On a full Linux kernel checkout — 65,163 files, 37.1 million lines — that’s
1.6 seconds and 7,873 noisy match lines for kmalloc. This server answers the same
question in 10 ms with 5 lines, by exposing GNU Global’s index to any MCP client
(Claude Code, Cursor, Claude Desktop) through 11 tools: definitions, references,
callers/callees, function bodies, reachability chains, and a blast_radius tool that
ranks what an uncommitted diff impacts.
It’s built for the codebases LSP tools can’t handle: trees that don’t compile, machines
without sudo, kernel-scale C/C++. The hard parts are the kernel-isms — #ifdef guard
stacks filtered against a .config, macro-generated symbols (sys_read resolved to its
SYSCALL_DEFINE3 site), and definitions gtags’ parser derails on recovered via their
EXPORT_SYMBOL site with ctags. Index management is invisible: the first query builds
it (66 s for the whole kernel), every query incrementally refreshes it in under a second.
Correctness is measured, not assumed: CI runs a 65-case golden eval against a pinned
kernel v6.16 on every push — currently 100% recall and 100% precision@1 across all 11
tools. Install is one user-level config entry (uvx mcp-gtags-server); the whole
toolchain bootstraps itself into user space on first use.
View this project as Markdown — plain text for LLMs and agents
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