by jgravelle
Symbol-level code retrieval for AI agents: fetch the function you need, not the whole file. Runs locally. 96.2% fewer tokens per lookup than grep-and-read on our 15-lookup benchmark. Works with Claude Code, Cursor, Codex and any MCP client.
# Add to your Claude Code skills
git clone https://github.com/jgravelle/jcodemunch-mcpGuides for using ai agents skills like jcodemunch-mcp.
Last scanned: 4/26/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-04-26T06:11:19.231Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}See how jcodemunch-mcp compares with popular alternatives.
jcodemunch-mcp is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by jgravelle. Symbol-level code retrieval for AI agents: fetch the function you need, not the whole file. Runs locally. 96.2% fewer tokens per lookup than grep-and-read on our 15-lookup benchmark. Works with Claude Code, Cursor, Codex and any MCP client. It has 2,817 GitHub stars.
Yes. jcodemunch-mcp passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/jgravelle/jcodemunch-mcp" and add it to your Claude Code skills directory (see the Installation section above).
jcodemunch-mcp is primarily written in Python. It is open-source under jgravelle on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh jcodemunch-mcp against similar tools.
No comments yet. Be the first to share your thoughts!
From $49 · 30 days · terms & refunds
⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
An MCP server that retrieves source code by symbol, not by file, using tree-sitter AST parsing. On our pinned benchmark, one lookup costs 96.2% fewer tokens than a grep-and-read agent (26.5x fewer; 86.9% to 98.7% across the 15 lookups), 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, 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.
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.
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.
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:
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.| 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 (15 task-runs) | 664,975 | 25,132 | 26.5x | 225.2x |
Against a grep-and-read agent: 96.2% reduction, 26.5x 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
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
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 Grep inside an indexed repo is answered from the index once: the hook refuse