jCodeMunch MCP

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 645B+ tokens saved · 95,000+ reporting installs · $3.2M+ in AI spend avoided · 77,000+ kg CO₂ prevented Counter figures as of 2026-08-05, 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.

Why 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.

Index once. Query cheaply. Keep moving. Precision context beats brute-force context.

Evidence

Reproducible token efficiency benchmark

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:

• Grep-top-3: 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.
• Read-all: every indexed source file concatenated. A ceiling nobody pays; retained for continuity with previously published figures.

Against a grep-and-read agent: 96.4% reduction, 27.9x fewer tokens. Per-query results range from 7.3x to 84.3x (median 25.5x); no single multiple describes every query. 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

Independent A/B test on a production codebase

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

Mentioned by

• Artur Skowroński (VirtusLab): "roughly 80% fewer tokens, or 5× more efficient — index once, query cheaply forever" · GitHub All-Stars #15
• Traci Lim (AWS · ASEAN AI Lead): "structural queries that native tools can't answer: find_importers, get_blast_radius, get_class_hierarchy, find_dead_code" · 5 Repos That Save Token Usage in Claude Code
• Julian Horsey (Geeky Gadgets): "3,850 tokens reduced to just 700 — a 5.5× improvement" · JCodeMunch AI Token Saver
• Eric Grill: "context is the scarce resource. Cut it by 90% and the whole stack gets cheaper and more reliable" · jCodemunch: Context Engine for AI Agents

Full recognition page →

Install

One-click installs

Recommended: one command

pip install jcodemunch-mcp
jcodemunch-mcp init

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.

Ubuntu 24.04+ / Debian 12+: system Python is externally managed (PEP 668). Use pipx install jcodemunch-mcp or uv tool install jcodemunch-mcp instead of bare pip install.

Verify:

jcodemunch-mcp --version

Manual Claude Code setup

pip install jcodemunch-mcp
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.

Quickstart

Full walkthrough: QUICKSTART.md. The two-minute version, inside your agent after init:

1. Ask: "Index this repo with jcodemunch."
2. Ask: "Using jcodemunch, find the function that handles authentication and show me its source."

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).

What you can do

• Retrieve one symbol instead of loading a file. 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 a whole task's context in one call. 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.
• Ask structural questions grep can't answer. find_importers, get_blast_radius, get_call_hierarchy, find_dead_code, get_changed_symbols, get_hotspots, search_ast anti-pattern sweeps, and more.
• Preflight risky changes. check_edit_safe, check_delete_safe, get_pr_risk_profile, and plan_refactoring with edit-ready {old_text, new_text} blocks.
• Trust the answers. Calibrated confidence scores, freshness flags, coverage contracts on absence claims, compiler-verified references via SCIP import, and automatic secret redaction before anything reaches the LLM.
• Keep the index fresh automatically. Watch modes, agent hooks, and a VS Code extension close the staleness gap.

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.

What's new

• v1.108.241 (2026-08-04) — offloadable-work annotation, off by default
• v1.108.240 (2026-08-04) — fresh stops meaning "we could not find out"
• v1.108.239 (2026-08-04) — a YAML key keeps its own name

When does it help (and when doesn't it)?

It helps most on targeted edits (one function, one method, one class), which is the majority of real editing work. Edits that genuinely require the entire file (restructuring file-level state, reordering logic spanning hundreds of lines) see no advantage. Best fits: large repositories, unfamiliar codebases, agent-driven exploration, refactoring and impact analysis, and teams cutting AI token costs without making agents dumber.

Languages: 70+ via tree-sitter, including Python, JavaScript/TypeScript, Go, Rust, Java, C/C++, C#, PHP, Ruby, Swift, and Kotlin. Full matrix: LANGUAGE_SUPPORT.md. Monorepos: yes; incremental indexing, workspace-member detection, subpath scoping.

Security, privacy, and background behavior

Local-first by design: indexes live at ~/.code-index/, and the base package's only default network behavior is an anonymous savings counter (random ID plus aggregate token counts, no code, no paths, no PII; opt out with share_savings: false). Everything the server does beyond answering a tool call (file watching, the opt-in login service, license validation, model downloads, org reporting) is opt-in or opt-out, visible, and reversible, and every item is enumerated in SECURITY.md alongside the path-traversal, symlink, and secret-redaction controls.

Documentation

Licensing and commercial use

jCodeMunch-MCP is released under the jCodeMunch-MCP Dual-Use License (full terms). Free for non-commercial use. Commercial use requires a paid license, one-time, sold by jMunch LLC via Stripe:

jCodeMunch-only: Builder, $79 (1 developer) · Studio, $349 (up to 5) · Platform, $1,999 (org-wide internal deployment)

Full jMunch suite (code + docs + data): Trio Builder, $99 · Trio Studio, $449 · Trio Platform, $2,499

Not sure it's worth it? Run your own numbers through the ROI calculator, or forward the finance-team version to whoever signs off. The guarantee stands: if jCodeMunch doesn't pay for itself, you don't pay for jCodeMunch.

Conditions on all uses: retain the copyright notice, clearly mark modifications and keep the original author's name intact (he's kinda full of himself), and include a prominent modification notice in source redistributions. The Software may not be renamed, rebranded, or published to any public package registry, and is provided "AS IS" without warranty. LICENSE controls.

FAQ

How much can I save on Claude / Opus tokens? In retrieval-heavy workflows, code-reading tokens typically drop 86-99%, benchmarked at 96.4% average (27.9x) against a grep-and-read agent across 15 tasks and 3 repositories. Per-query results span 7.3x to 84.3x. Methodology: TOKEN_SAVINGS.md and benchmarks/.

How is this different from RAG or grep-based tools? jCodeMunch retrieves at the symbol level with byte-level precision (functions, classes, importers, blast radius, hierarchies) rather than fuzzy chunks (RAG) or raw line matches (grep) the agent still has to read and reason over.

Is it free for personal use? Yes. Commercial use needs a license; see above.

Where's the deep-dive on X? Capabilities: CAPABILITIES.md. Config: CONFIGURATION.md. Clients: CLIENTS.md. Internals: UNDER_THE_HOOD.md. Or the firehose: jcodemunch.com.

Extras: OSS code-health observatory (weekly six-axis snapshots of Express, FastAPI, Gin, Django, and friends) · Token Cost Radar (daily AI token cost intelligence) · jMunch Console (free MIT GUI for one-click upgrades)