Cut AI token costs 95%+ on code exploration. The leading MCP server for precise, symbol-level GitHub code retrieval via tree-sitter AST. Works with Claude Code,
Cut AI token costs 95%+ on code exploration. The leading MCP server for precise, symbol-level GitHub code retrieval via tree-sitter AST. Works with Claude Code,
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 28.3x 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 possibleget_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 2026-09-03 on v1.108.316. 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.Against a grep-and-read agent: 96.5% reduction, 28.3x 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), writes their config entries, installs the CLAUDE.md prompt policy so your agent actually uses jCodeMunch, optionally installs enforcement hooks, optionally indexes your project, and audits your agent config files for token waste.
uvx jcodemunch-mcp
Zero install. Runs from an ephemeral environment — nothing lands on disk permanently. The client entries init writes already invoke the server this way, so for most setups this is all that ever runs. ⚠ Enforcement hooks are the exception: they're spawned by a minimal-PATH subshell and resolve the executable by name, so they need uv tool install (or pipx/pip) to work.
pipx install jcodemunch-mcp
You already standardise on pipx
pip install jcodemunch-mcp
Inside a virtualenv you manage yourself
Verify:
jcodemunch-mcp --version
claude mcp add -s user jcodemunch -- uvx jcodemunch-mcp
No install step — uvx fetches and runs the server on demand. Prefer it on your PATH (and required for enforcement hooks)? uv tool install jcodemunch-mcp, then claude mcp add -s user jcodemunch jcodemunch-mcp.
Then tell the agent to prefer the tools. This matters more than people think; installation makes the tools available but does not break the agent's brute-reading habit. One line in your CLAUDE.md does it:
Call the jcodemunch_guide tool and strictly follow its instructions.
Using Cursor, Windsurf, Codex CLI, Antigravity, Gemini CLI, Qwen Code, Kiro, Cline, Zed, Goose, Hermes, Odysseus, or Paperclip? Every tested client configuration lives in CLIENTS.md. Optional extras (local semantic search, AI summaries per provider) are in QUICKSTART.md; the system surfaces each extra pulls in are documented in SECURITY.md.
Full walkthrough: QUICKSTART.md. The two-minute version, inside your agent after init:
The agent should answer via search_symbols and get_symbol_source, returning tens of lines instead of whole files. Confirm with get_session_stats: it reports tokens served and savings for the session. That is where the numbers on the meter come from.
Want to skip initial indexing for popular frameworks? Pre-built starter packs: jcodemunch-mcp install-pack --list (free packs need no license).
get_symbol_source returns the exact function body, byte-precise, for the majority of edits that touch one function in a 700-line file (~95% savings on that read).assemble_task_context classifies the task intent, extracts anchor symbols, and runs the right tool sequence under one token budget. plan_turn routes the turn before the first read.find_importers, get_blast_radius, get_call_hierarchy, find_dead_code, get_changed_symbols, get_hotspots, search_ast anti-pattern sweeps, and more. Two of them sound alike and are not: check_references answers where a name is used (import sites plus every file whose content mentions it), find_references answers who imports it, over the import graph alone, so a call site is invisible to it.check_edit_safe, check_delete_safe, get_pr_risk_profile, and plan_refactoring with edit-ready {old_text, new_text} blocks. The two safety checks return stop_rule.terminal: true means no further jcodemunch call moves the verdict, so re-running find_importers or check_references to be sure is wasted work. It means final, not safe. Hand the server your type checker's own output (jcodemunch-mcp import-trace --diagnostics <file>: mypy --output json, pyright --outputjson, tsc --pretty false, ruff --output-format json) and check_edit_safe, get_changed_symbols, get_pr_risk_profile and get_symbol_provenance say which symbols the checker already flags, as of which commit. Nothing runs a checker for you. False names the specific thing that would change the answer.That's the highlight reel. The complete tour of 90+ tools, the MUNCH compact wire format, evidence receipts, offloadable-work annotation, and the session-economics instrumentation is in CAPABILITIES.md, with internals in UNDER_THE_HOOD.md.
| Scenario | Native tool | jCodeMunch | Savings | |----------|-----------
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