Structured code retrieval for serious AI agents
Quickstart - https://github.com/jgravelle/jcodemunch-mcp/blob/main/QUICKSTART.md
FREE FOR PERSONAL USE
Use it to make money, and Uncle J. gets a taste. Fair enough? details
Cut code-reading token usage by **95% or more**
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.
In retrieval-heavy workflows, that routinely cuts code-reading token usage by 95%+ because the agent stops brute-reading giant files just to find one useful implementation.
| 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 |
Index once. Query cheaply. Keep moving. Precision context beats brute-force context.
jCodeMunch MCP
Structured code retrieval for serious AI agents
Commercial licenses
jCodeMunch-MCP is free for non-commercial use.
Commercial use requires a paid license.
jCodeMunch-only licenses
- Builder — $79 — 1 developer
- Studio — $349 — up to 5 developers
- Platform — $1,999 — org-wide internal deployment
Want both code and docs retrieval?
Stop paying your model to read the whole damn file.
jCodeMunch turns repo exploration into structured retrieval.
Instead of forcing an agent to open giant files, wade through imports, boilerplate, comments, helpers, and unrelated code, jCodeMunch lets it navigate by what the code is and retrieve only what matters.
That means:
- 95%+ lower code-reading token usage in many retrieval-heavy workflows
- less irrelevant context polluting the prompt
- faster repo exploration
- more accurate code lookup
- less repeated file-scanning nonsense
It indexes your codebase once using tree-sitter, stores structured symbol metadata plus byte offsets into the original source, and retrieves exact implementations on demand instead of re-reading entire files over and over.
Recent releases have also made that retrieval workflow sharper and more useful in real engineering work, with BM25-based symbol search, context bundles, compact search modes, query suggestions for unfamiliar repos, dependency graphs, class hierarchy traversal, blast-radius analysis, multi-symbol bundles, live watch-based reindexing, automatic Claude Code worktree discovery (watch-claude), and benchmark reproducibility improvements.
Real-world results
Independent 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:
| Metric | Native | JCodeMunch |
|---|---|---|
| Success rate | 72% | 80% |
| Timeout rate | 40% | 32% |
| Mean cost/iteration | $0.783 | $0.738 |
| Mean cache creation | 104,135 | 93,178 (−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`
Why agents need this
Tools (3)
search_symbolsSearch for symbols within the codebase using BM25-based search.find_importersIdentify files that import a specific symbol or module.get_symbol_implementationRetrieve the exact implementation of a specific symbol.Configuration
{"mcpServers": {"jcodemunch": {"command": "uvx", "args": ["jcodemunch-mcp"]}}}