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 28.9x 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 2026-10-14 on v1.108.360. 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.