// 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.

SOURCE CODEgithub.com/harshithsunku/mcp-gtags-serverOpen on GitHub ↗

// MORE IN AI & AGENTS

Agent loops, courses, and plumbing for LLMs.

All projects →
AI & AGENTSPython · Jupyter

Learn Jev end to end

A free, hands-on course on building AI agents with a fast brain and a slow brain — 12 short notebooks and 13 real use cases that put Jev, a decision model, inside an LLM agent loop. One API key; the whole course runs for under $0.20.

PythonJupyterLLMsAgentsOpenRouter
AI & AGENTSPython · Jupyter

Build Your First AI Agent

A twelve-notebook course that builds an AI agent from scratch — a hand-rolled ~35-line agent loop, then RAG, MCP, structured outputs, memory, multi-agent orchestration, and evals layered on top. Works with any OpenAI-compatible provider.

PythonJupyterLLMsMCPRAG