World Monitor
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jCodeMunch MCP
Overview
Description from the maker (original English)
Gives AI agents the function they asked for, not the file. Commercial use needs a license.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi2 of 2 passedChecked from public information; code not scanned yet
- UsageiWidely used↓ 20.2K/wk · ★ 2,840
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Other
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 2 of 2 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"jcodemunch": {
"command": "uvx",
"args": [
"jcodemunch-mcp"
]
}
}
}Run this in your terminal
claude mcp add jcodemunch -- uvx jcodemunch-mcpOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"jcodemunch": {
"command": "uvx",
"args": [
"jcodemunch-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"jcodemunch": {
"type": "stdio",
"command": "uvx",
"args": [
"jcodemunch-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 10, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account jgravelle, which owns the repo
- ✓ Pass
Still maintained
Code last changed today
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Otheri
- More stars than 100% of the other 19071 servers in this category
- Published to the registry more recently than 99% of the other 19071 servers in this category
- Basic checks equal to or better than 100% of the other 19071 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is jCodeMunch MCP safe?
It passed 2 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed today, and the latest version (1.108.431) reached the registry today.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: World Monitor, Claude Flow, Tldraw
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Claude Flow
AI orchestration with hive-mind swarms, neural networks, and 87 MCP tools for enterprise dev.
Runs on your computer
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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.
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?" | Search for the name, then read each hit | get_blast_radius: affected files and symbols, across indexed repos, with no IDE running |
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 2026-10-22 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 -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 | 989 | 15,724 avg | 990 avg | 15.9x | 156.1x |
| fastapi/fastapi | 1,186 | 13,699 | 85,296 avg | 2,556 avg | 33.4x | 322.9x |
| gin-gonic/gin | 98 | 1,822 | 31,975 avg | 1,481 avg | 21.6x | 102.5x |
| Grand total, pooled (15 task-runs) | 664,975 | 25,132 | 26.5x | 225.2x |
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
Install
One-click installs
Recommended: one command
uv tool install jcodemunch-mcpjcodemunch-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
| 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 --versionManual Claude Code setup
claude mcp add -s user jcodemunch -- uvx jcodemunch-mcpNo 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.
The