The leading, most token-efficient MCP server for GitHub source code exploration via tree-sitter AST parsing
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Jcodemunch MCP is an MCP server that the leading, most token-efficient MCP server for GitHub source code exploration via tree-sitter AST parsing. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 91/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"jdocmunch": {
"args": [
"jdocmunch-mcp"
],
"type": "stdio",
"command": "uvx"
},
"jcodemunch": {
"args": [
"jcodemunch-mcp"
],
"type": "stdio",
"command": "uvx"
}
}
}Are you the author?
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The most token-efficient MCP server for precise source code retrieval via tree-sitter AST parsing. Cut AI token costs 86-99% on code exploration (96% average, benchmarked at 27.9x fewer tokens than a grep-and-read agent) and stop burning your context window reading entire files.
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.
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?" | Not possible | get_blast_radius |
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 2026-08-03 on v1.108.233. 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 | |------------|------:|--------:|-------