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.
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.
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 -lthe 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 | 1,017 avg | 15.5x | 152.0x |
| fastapi/fastapi | 1,186 | 13,240 | 85,296 avg | 2,218 avg | 38.4x | 372.0x |
| gin-gonic/gin | 98 | 1,451 | 31,975 avg | 1,573 avg | 20.3x | 96.5x |
| 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. Per-query results range from 7.6x to 81.2x (median 26.1x); no single multiple describes every query. 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
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 distros (Ubuntu 24.04+, Debian 12+) where bare pip install is refused. Don't have uv yet?
init auto-detects your MCP clients (Claude Code, Claude Desktop, Cursor, Windsurf, Continue), writes their config entries, installs the CLAUDE.md prompt policy so your agent actually uses jCodeMunch, optionally installs enforcement hooks, optionally indexes your project, and audits your agent config files for token waste.
| Command | Use it when |
|---|---|
uvx jcodemunch-mcp |
Zero install. Runs from an ephemeral environment — nothing lands on disk permanently. The client entries init writes already invoke the server this way, so for most setups this is all that ever runs. ⚠ Enforcement hooks are the exception: they're spawned by a minimal-PATH subshell and resolve the executable by name, so they need uv tool install (or pipx/pip) to work. |
pipx install jcodemunch-mcp |
You already standardise on pipx |
pip install jcodemunch-mcp |
Inside a virtualenv you manage yourself |
Verify:
jcodemunch-mcp --version
Manual Claude Code setup
claude mcp add -s user jcodemunch -- uvx jcodemunch-mcp
No install step — uvx fetches and runs the server on demand. Prefer it on your PATH (and required for enforcement hooks)? uv tool install jcodemunch-mcp, then claude mcp add -s user jcodemunch jcodemunch-mcp.
Then tell the agent to prefer the tools. This matters more than people think; installation makes the tools available but does not break the agent's brute-reading habit. One line in your CLAUDE.md does it:
Call the jcodemunch_guide tool and strictly follow its instructions.
Using Cursor, Windsurf, Codex CLI, Antigravity, Gemini CLI, Qwen Code, Kiro, Cline, Zed, Goose, Hermes, Odysseus, or Paperclip? Every tested client configuration lives in CLIENTS.md. Optional extras (local semantic search, AI summaries per provider) are in QUICKSTART.md; the system surfaces each extra pulls in are documented in SECURITY.md.
Quickstart
Full walkthrough: QUICKSTART.md. The two-minute