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/
FREE FOR PERSONAL USE
Use it to make money, and Uncle J. gets a taste. Fair enough? details
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.
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.
jCodeMunch MCP
Structured code retrieval for serious AI agents
What's new
- v1.108.55 (2026-06-12) — turn-budget reset, honest budget packing, fast search_text rejection (#327, #328, #329)
- v1.108.54 (2026-06-11) — list-repos / delete-index honor CODE_INDEX_PATH
- v1.108.53 (2026-06-11) — recency window on weight tuning
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 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 made that retrieval workflow sharper and more useful in real engineering work, with BM25-based symbol search, fuzzy matching, semantic/hybrid search (opt-in, zero mandatory dependencies), query-driven token-budgeted context assembly (get_ranked_context), dead code detection (`find_dead_co