jgravelle / jcodemunch-mcp

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?** > > * Munch Duo Builder Bundle — $89 > * Munch Duo Studio Bundle — $399 > * Munch Duo Platform Bundle — $2,249 **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 ( ), 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 — a structural query native tools cannot answer without scripting. Full report: --- Why agents need this Most agents still inspect codebases like tourists trapped in an airport gift shop: • open entire files to find one function • re-read the same code repeatedly • consume imports, boilerplate, and unrelated helpers • burn context window on material they never needed in the first place jCodeMunch fixes that by giving them a structured way to: • search symbols by name, kind, or language • inspect file and repo outlines before pulling source • retrieve exact symbol implementations only • grab a context bundle when surrounding imports matter • fall back to text search when structure alone is not enough Agents do not need bigger and bigger context windows. They need **better aim**. --- What you get Symbol-level retrieval Find and fetch functions, classes, methods, constants, and more without opening entire files. Faster repo understanding Inspect repository structure and file outlines before asking for source. Lower token spend Send the model the code it needs, not 1,500 lines of collateral damage. Structural queries native tools can't answer tells you what imports a file. tells you what breaks if you change a symbol. traverses inheritance chains. These are not "faster grep" — they are questions grep cannot answer at all. Better engineering workflows Useful for onboarding, debugging, refactoring, impact analysis, and exploring unfamiliar repos without brute-force file reading. Local-first speed Indexes are stored locally for fast repeated access. --- How it works jCodeMunch indexes local folders or GitHub repos, parses source with tree-sitter, extracts symbols, and stores structured metadata alongside raw file content in a local index. Each symbol includes enough information to be found cheaply and retrieved precisely later. That includes metadata like: • signature • kind • qualified name • one-line summary • byte offsets into the original file So when the agent wants a symbol, jCodeMunch can fetch the exact source directly instead of loading and rescanning the full file. --- Start fast • Install it • Add it to your MCP client If you’re using Claude Code: • Tell your agent to actually use it This matters more than people think. Installing jCodeMunch makes t…