jCodeMunch MCP

jCodeMunch MCP

An MCP server that retrieves source code by symbol, not by file, using tree-sitter AST parsing. It uses 94.5% fewer tokens per lookup on average than grep-and-read on our 15-lookup benchmark (86.9% to 98.7% across the lookups, at pinned commits), so your agent stops spending its context window on whole files.

Benchmark: 5 queries on each of express, fastapi and gin at pinned commits, tokens counted with tiktoken cl100k_base, jcodemunch-mcp at commit 15afcf75, whose version string is 1.108.391 but which also carries the settings-file ranking change released in 1.108.392 and the field/constant change released after it. A lookup is one search_symbols plus get_symbol_source on its top 3 hits, against grep-and-read (rank files by match count, read the top 3 whole). Per lookup, not per session. Reproduce with benchmarks/REPRODUCING.md.

Real results from production, as of 2026-08-17

838B+ tokens saved · 136,000+ reporting installs · $4.2M+ in AI spend avoided · 100,000+ kg CO₂ prevented

Estimated counter figures as of 2026-08-17, not live, valued at the $5/MTok Claude Opus input rate. All four only grow, so read them as floors. Live at [jcodemunch.com](https://jcodemunch.com/?utm_medium=readme&utm_campaign=jcm_readme_counter).

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.

Small by default. A new install starts on a six-tool front door: menu, order and route do the work, and jcodemunch_guide, announce_model and set_tool_tier help. The full tool set sits behind it, and jcodemunch-mcp surface shows exactly what your install advertises.

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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.

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 2026-10-22 on v1.108.391 (at commit 15afcf75, whose version string is 1.108.391 but which also carries the settings-file ranking change released in 1.108.392 and the field/constant change released after it). 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.

Against a grep-and-read agent: 94.5% fewer tokens per lookup on average. 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 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, Codex, opencode, Gemini CLI, Cline, VS Code with Copilot), writes their config entries, installs the hooks that keep the index fresh without adding anything to the model's context (re-index after an edit, a pre-compaction checkpoint, a task-complete note to you), optionally indexes your project, and audits your agent config files for token waste. Two extras are opt-in because the model reads them on every turn they apply: --policy writes the Code Exploration Policy into CLAUDE.md, AGENTS.md and the Cursor and Windsurf rules files, and --guidance adds the hooks that advise the model on Read/Grep/Glob/Bash, brief subagents and restore context after compaction. With the PreToolUse hook installed (--guidance or --strict) and JCODEMUNCH_HOOK_GREP_ANSWER=1, a large Grep inside an indexed repo is answered from the index once: the hook refuses it with every match counted as the reason (by file and by function or class, with less detail for a very large set; the lines themselves are not included; the answer says it came from the index, how many lines in how many files match the pattern case-insensitively, and that JCODEMUNCH_HOOK_GREP_ANSWER=0 turns it off), and the same Grep sent again runs normally. A Grep under the size thresholds (JCODEMUNCH_HOOK_GREP_ANSWER_MIN_LINES, JCODEMUNCH_HOOK_GREP_ANSWER_MIN_FILES) runs untouched, and so does any Grep on an index the file cap cut short. It is off by default. An install that already has them keeps them; jcodemunch-mcp init --remove-guidance removes them, with backups.

Ask for it by name. Tell your agent to use jcodemunch ("use jcodemunch to find where X is handled"). In the sessions we measured, agents used it when the user asked for it by name.

Other install paths

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.

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