jCodeMunch MCP
The leading, most token-efficient MCP server for precise GitHub source code retrieval via tree-sitter AST parsing. Cut AI token costs 95%+ on code exploration — stop burning your context window reading entire files.
Real results, live from production 335B+ tokens saved · 48,000+ developers · $1.69M+ in AI spend avoided · 40,000+ kg CO₂ prevented Live telemetry at jcodemunch.com — benchmark: 95% average token reduction (15 tasks / 3 repos, 99.8% peak).
Works with Autohand Code, Claude Code, Cursor, VS Code, Codex CLI, Continue, Windsurf, and any MCP-compatible client.
One-click installs:
Prefer the command line?
pip install jcodemunch-mcp
uvx jcodemunch-mcp
For pinned/B2B deployments that want a version-stable install channel independent of PyPI, install straight from the repo (requires git, builds from source):
pip install git+https://github.com/jgravelle/jcodemunch-mcp.git
uvx --from git+https://github.com/jgravelle/jcodemunch-mcp.git jcodemunch-mcp
Quickstart - https://github.com/jgravelle/jcodemunch-mcp/blob/main/QUICKSTART.md
A crapload of detailed info: http://jcodemunch.com/
Live OSS code-health observatory — weekly six-axis health snapshots of Express, FastAPI, Gin, Pydantic, Django, Flask, NestJS, Cobra, and this very repo: https://jgravelle.github.io/jcodemunch-observatory/
Token Cost Radar — daily intelligence on AI token costs, minimization strategies, and budget trends for teams running Claude Code / Cursor / MCP: https://jcodemunch.com/radar/
FREE FOR PERSONAL USE
Use it to make money, and Uncle J. gets a taste. Fair enough? details
Our guarantee: If jCodeMunch doesn't pay for itself, you don't pay for jCodeMunch!
Cut code-reading token usage by 95% or more with precise symbol retrieval
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.
Documentation
| Doc | What it covers |
|---|---|
| QUICKSTART.md | Zero-to-indexed in three steps |
| USER_GUIDE.md | Full tool reference, workflows, and best practices |
| AGENT_HOOKS.md | Agent hooks and prompt policies |
| CONFIGURATION.md | JSONC config file reference, migration from env vars |
| GROQ.md | Groq Remote MCP integration, deployment, gcm CLI |
| HEADLESS.md | Using jCodeMunch with claude -p (and the jragmunch CLI) |
| ARCHITECTURE.md | Internal design, storage model, and extension points |
| LANGUAGE_SUPPORT.md | Supported languages and parsing details |
| CONTEXT_PROVIDERS.md | dbt, Git, and custom context provider docs |
| TROUBLESHOOTING.md | Common issues and fixes |
| AGENT_INSTALL_UNIVERSAL.md | Paste-and-go prompt for installing jCodemunch guidance into agent/IDE clients without a first-class jcm install target (Codex CLI, Cline, JetBrains AI, Aider, etc.). For Claude Code, Cursor, Windsurf, Continue — use jcm install <client> instead. |
Compact output — the second token axis (MUNCH)
Retrieval decides what to send. MUNCH decides how to pack it.
Every tool response can be emitted in a purpose-built compact wire format instead of verbose JSON. Path prefixes are interned to short handles, homogeneous lists of dicts pack into single-character-tagged CSV rows, and per-column types are preserved so the decode is lossless.
# any tool call accepts format=
find_references(identifier="get_user", format="auto")
# auto — emit compact if savings ≥ 15%, otherwise JSON
# compact — always compact
# json — never compact (back-compat passthrough)
Benchmark (v1.56.0): median 45.5% bytes saved across 6 representative tools, peaks at 55.4% on graph and outline responses. Full spec in SPEC_MUNCH.md; numbers and harness in TOKEN_SAVINGS.md.
Encoding savings stack on top of retrieval savings — every byte off the wire is a byte the agent doesn't pay to read.
Structured code retrieval for serious AI agents
What's new
- v1.108.94 (2026-07-02) — New tool: index_dependency (index the libraries a repo actually uses)
- v1.108.93 (2026-07-02) — Upstream exposure links (get_endpoint_impact include_infra exposes)
- v1.108.92 (2026-07-02) — Progress-notification flood control + response drain (#359)
Mentioned by
- Artur Skowroński (VirtusLab) — "roughly 80% fewer tokens, or 5× more efficient — index once, query cheaply forever" · GitHub All-Stars #15
- Julian Horsey (Geeky Gadgets) — "3,850 tokens reduced to just 700 — a 5.5× improvement" · JCodeMunch AI Token Saver
- Sion Williams — "preserving tokens for tasks that actually require reasoning rather than retrieval" · March 2026 AI Workflow Update
- 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
- 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
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 the full jMunch suite (code + docs + data)?
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 t