Quickstart - https://github.com/jgravelle/jcodemunch-mcp/blob/main/QUICKSTART.md

FREE FOR PERSONAL USE

Use it to make money, and Uncle J. gets a taste. Fair enough? details

Documentation

| Doc | What it covers | |-----|----------------| | QUICKSTART.md | Zero-to-indexed in three steps | | USER_GUIDE.md | Full tool reference, workflows, and best practices | | AGENT_HOOKS.md | Agent hooks and prompt policies | | ARCHITECTURE.md | Internal design, storage model, and extension points | | LANGUAGE_SUPPORT.md | Supported languages and parsing details | | CONTEXT_PROVIDERS.md | dbt, Git, and custom context provider docs | | TROUBLESHOOTING.md | Common issues and fixes |

Cut code-reading token usage by 95% or more

Most AI agents explore repositories the expensive way:

open entire files → skim thousands of irrelevant lines → repeat.

That is not “a little inefficient.” That is a token incinerator.

jCodeMunch indexes a codebase once and lets agents retrieve only the exact code they need: functions, classes, methods, constants, outlines, and tightly scoped context bundles, with byte-level precision.

In retrieval-heavy workflows, that routinely cuts code-reading token usage by 95%+ because the agent stops brute-reading giant files just to find one useful implementation.

| Task | Traditional approach | With jCodeMunch | | ---------------------- | ------------------------- | ------------------------------------------- | | Find a function | Open and scan large files | Search symbol → fetch exact implementation | | Understand a module | Read broad file regions | Pull only relevant symbols and imports | | Explore repo structure | Traverse file after file | Query outlines, trees, and targeted bundles |

Index once. Query cheaply. Keep moving. Precision context beats brute-force context.

jCodeMunch MCP

Structured code retrieval for serious AI agents

Mentioned by

• Artur Skowroński (VirtusLab) — "roughly 80% fewer tokens, or 5× more efficient — index once, query cheaply forever" · GitHub All-Stars #15
• Julian Horsey (Geeky Gadgets) — "3,850 tokens reduced to just 700 — a 5.5× improvement" · JCodeMunch AI Token Saver
• Sion Williams — "preserving tokens for tasks that actually require reasoning rather than retrieval" · March 2026 AI Workflow Update
• Traci Lim (AWS · ASEAN AI Lead) — "structural queries that native tools can't answer: find_importers, get_blast_radius, get_class_hierarchy, find_dead_code" · 5 Repos That Save Token Usage in Claude Code
• Eric Grill — "context is the scarce resource. Cut it by 90% and the whole stack gets cheaper and more reliable" · jCodemunch: Context Engine for AI Agents

[Full recognition page →](https://j.gravelle.us/jCodeMunch/recog

... View full README on GitHub