jgravelle/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 29.2x 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 2026-10-15 on v1.108.364. 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 | 989 | 15,724 avg | 967 avg | **16.3x** | 159.8x |
| fastapi/fastapi | 1,186 | 13,699 | 85,296 avg | 2,097 avg | **40.7x** | 393.6x |
| gin-gonic/gin | 98 | 1,822 | 31,975 avg | 1,484 avg | **21.5x** | 102.3x |
| **Grand total (15 task-runs)** | | | **664,975** | **22,741** | **29.2x** | 248.8x |
**Against a grep-and-read agent: 96.6% reduction, 29.2x 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