jCodeMunch MCP

The most token-efficient MCP server for precise source code retrieval via tree-sitter AST parsing. Cut AI token costs 86-99% on code exploration (96% average, benchmarked at 28.3x fewer tokens than a grep-and-read agent) and stop burning your context window reading entire files.

Real results, live from production 838B+ tokens saved · 136,000+ reporting installs · $4.2M+ in AI spend avoided · 100,000+ kg CO₂ prevented Counter figures as of 2026-08-17, valued at the $5/MTok Claude Opus input rate. All four only grow, so read them as floors. Live at jcodemunch.com.

Works with Claude Code, Cursor, VS Code, Codex CLI, Windsurf, Continue, and any MCP-compatible client.

Install now · Quickstart · See the evidence · Pricing

Free for personal use. Use it to make money, and Uncle J. gets a taste. Fair enough? Commercial licenses below. Our guarantee: if jCodeMunch doesn't pay for itself, you don't pay for jCodeMunch.

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Why jCodeMunch?

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. It parses source with tree-sitter, stores structured symbol metadata (signature, kind, qualified name, summary, byte offsets) alongside raw file content in a local index, and fetches exact implementations on demand instead of re-reading files over and over.

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
"What breaks if I change X?"	Not possible	get_blast_radius

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

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Evidence

Reproducible token efficiency benchmark

Measured with tiktoken cl100k_base across three public repos pinned to upstream commits, run 2026-09-03 on v1.108.316. Workflow: search_symbols (top 5) + get_symbol_source × 3 per query. Two baselines, same run, same corpus, same file reader:

• Grep-top-3: rg -l the query terms, rank files by match count, open the top 3 whole. This is what a competent agent without the tool actually does, and it is the number to quote.
• Read-all: every indexed source file concatenated. A ceiling nobody pays; retained for continuity with previously published figures.

Repository	Files	Symbols	Grep-top-3 baseline	jCodeMunch	vs grep	vs read-all
expressjs/express	186	455	15,724 avg	1,007 avg	15.6x	153.5x
fastapi/fastapi	1,186	13,240	85,296 avg	2,149 avg	39.7x	384.1x
gin-gonic/gin	98	1,451	31,975 avg	1,537 avg	20.8x	98.8x
Grand total (15 task-runs)			664,975	23,467	28.3x	241.1x

Against a grep-and-read agent: 96.5% reduction, 28.3x fewer tokens. No single multiple describes every query; the per-repo rows above are the spread. Against read-all the figure is 99.6%, but nobody pays that ceiling. Compact MUNCH wire encoding then trims a median 45.5% more bytes off responses.

Full methodology, pinned commits, harness, and known caveats: benchmarks/METHODOLOGY.md · Reproduce it yourself · TOKEN_SAVINGS.md

Independent A/B test on a production codebase

50-iteration A/B test on a real Vue 3 + Firebase production codebase, jCodeMunch vs native tools (Grep/Glob/Read), Claude Sonnet 4.6, fresh session per iteration: success rate 80% vs 72%, timeout rate 32% vs 40%, mean cache creation down 10.5%. Tool-layer savings isolated from fixed overhead: 15-25%. One finding category appeared exclusively in the jCodeMunch variant: orphaned file detection via find_importers, a structural query native tools cannot answer without scripting. Full report: benchmarks/ab-test-naming-audit-2026-03-18.md

Mentioned by

• Artur Skowroński (VirtusLab): "roughly 80% fewer tokens, or 5× more efficient — index once, query cheaply forever" · GitHub All-Stars #15
• 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
• Julian Horsey (Geeky Gadgets): "3,850 tokens reduced to just 700 — a 5.5× improvement" · JCodeMunch AI Token Saver
• 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 →

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Install

One-click installs

Recommended: one command

uv tool install jcodemunch-mcp
jcodemunch-mcp init

No virtualenv to manage, nothing written into system Python, and it works as-is on PEP 668