jCodemunch MCP
Token-efficient code exploration via tree-sitter AST parsing. 25+ languages, 95%+ token savings.
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README
Quickstart - https://github.com/jgravelle/jcodemunch-mcp/blob/main/QUICKSTART.md <!-- mcp-name: io.github.jgravelle/jcodemunch-mcp --> ## FREE FOR PERSONAL USE **Use it to make money, and Uncle J. gets a taste. Fair enough?** [details](#commercial-licenses) --- ## Documentation | Doc | What it covers | |-----|----------------| | [QUICKSTART.md](QUICKSTART.md) | Zero-to-indexed in three steps | | [USER_GUIDE.md](USER_GUIDE.md) | Full tool reference, workflows, and best practices | | [AGENT_HOOKS.md](AGENT_HOOKS.md) | Agent hooks and prompt policies | | [ARCHITECTURE.md](ARCHITECTURE.md) | Internal design, storage model, and extension points | | [LANGUAGE_SUPPORT.md](LANGUAGE_SUPPORT.md) | Supported languages and parsing details | | [CONTEXT_PROVIDERS.md](CONTEXT_PROVIDERS.md) | dbt, Git, and custom context provider docs | | [TROUBLESHOOTING.md](TROUBLESHOOTING.md) | Common issues and fixes | --- ## 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      [](https://pypi.org/project/jcodemunch-mcp/) [](https://pypi.org/project/jcodemunch-mcp/) > ## Commercial licenses > > jCodeMunch-MCP is **free for non-commercial use**. > > **Commercial use requires a paid license.** > > **jCodeMunch-only licenses** > > * [Builder — $79](https://j.gravelle.us/jCodeMunch/descriptions.php#builder) — 1 developer > * [Studio — $349](https://j.gravelle.us/jCodeMunch/descriptions.php#studio) — up to 5 developers > * [Platform — $1,999](https://j.gravelle.us/jCodeMunch/descriptions.php#platform) — org-wide internal deployment > > **Want both code and docs retrieval?** > > * [Munch Duo Builder Bundle — $89](https://j.gravelle.us/jCodeMunch/descriptions.php#builder) > * [Munch Duo Studio Bundle — $399](https://j.gravelle.us/jCodeMunch/descriptions.php#studio) > * [Munch Duo Platform Bundle — $2,249](https://j.gravelle.us/jCodeMunch/descriptions.php#platform) **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,