Quickstart - https://github.com/jgravelle/jcodemunch-mcp/blob/main/QUICKSTART.md

Use it to make money, and Uncle J. gets a taste. Fair enough? details

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.

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

• 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

Full recognition page →

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 both code and docs retrieval?

• Munch Duo Builder Bundle — $89
• Munch Duo Studio Bundle — $399
• Munch Duo Platform Bundle — $2,249

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_code), untested symbol detection (get_untested_symbols), git-diff-to-symbol mapping (get_changed_symbols), architectural centrality ranking (get_symbol_importance, PageRank), blast-radius depth scoring with source snippets, context bundles with token budgets, AST-derived call graphs and call hierarchy traversal, decorator-aware search and filtering, hotspot detection (complexity x churn), dependency cycles and coupling metrics, session-aware routing (plan_turn, turn budgets, negative evidence), agent config auditing, complexity-based model routing (Agent Selector), enforcement hooks (PreToolUse/PostToolUse/PreCompact), dependency graphs, class hierarchy traversal, multi-symbol bundles, live watch-based reindexing, automatic Claude Code worktree discovery (watch-claude), auto-watch on demand (when watch: true in config, the server automatically indexes and watches any repo a tool is called against — ensuring fresh results from the first call), and trusted-folder access controls.

Measured with tiktoken cl100k_base across three public repos. Workflow: search_symbols (top 5) + get_symbol_source × 3 per query. Baseline: all source files concatenated (minimum cost for an agent that reads everything). Full methodology and harness →

Per-query results range from 79.7% (dense FastAPI router query) to 99.8% (sparse context-bind query on Express). The 95% figure is the aggregate. Run python benchmarks/harness/run_benchmark.py to reproduce.

Independent 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:

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

Most agents still inspect codebases like tourists trapped in an airport gift shop:

• open entire files to find one function
• re-read the same code repeatedly
• consume imports, boilerplate, and unrelated helpers
• burn context window on material they never needed in the first place

jCodeMunch fixes that by giving them a structured way to:

• search symbols by name, kind, or language — with fuzzy matching and optional semantic/hybrid search
• inspect file and repo outlines before pulling source
• retrieve exact symbol implementations only
• grab a token-budgeted context bundle or ranked context pack for a task
• fall back to text search when structure alone is not enough
• detect dead code, trace impact, rank by centrality, and map git diffs to symbols
• plan the next turn with plan_turn — confidence-guided routing before the first read
• track session state and avoid re-reading files the agent already explored

Agents do not need bigger and bigger context windows.

They need better aim.

Find and fetch functions, classes, methods, constants, and more without opening entire files.

Inspect repository structure and file outlines before asking for source.

Send the model the code it needs, not 1,500 lines of collateral damage.

find_importers tells you what imports a file. get_blast_radius tells you what breaks if you change a symbol, with depth-weighted risk scores and optional source snippets. get_class_hierarchy traverses inheritance chains. get_call_hierarchy traces callers and callees N levels deep using AST-derived call graphs, with optional LSP-enriched dispatch resolution for interface/trait method calls. find_dead_code finds symbols and files unreachable from any entry point. get_untested_symbols finds functions with no evidence of test-file reachability — the intersection of import-graph analysis and test-file detection. get_changed_symbols maps a git diff to the exact symbols that were added, modified, or removed. get_symbol_importance ranks your codebase by architectural centrality using PageRank on the import graph. get_hotspots surfaces the riskiest code by combining complexity with git churn. get_dependency_cycles detects circular imports. get_coupling_metrics measures module coupling and instability. These are not "faster grep" — they are questions grep cannot answer at all.

audit_agent_config scans your CLAUDE.md, .cursorrules, copilot-instructions.md, and other agent config files for token waste: per-file token cost, stale symbol references (cross-referenced against the index — catches renamed or deleted functions), dead file paths, redundancy between global and project configs, bloat, and scope leaks. No other tool can tell you "line 15 references a function that was renamed three weeks ago."

Useful for onboarding, debugging, refactoring, impact analysis, and exploring unfamiliar repos without brute-force file reading.

Indexes are stored locally for fast repeated access.

jCodeMunch indexes local folders or GitHub repos, parses source with tree-sitter, extracts symbols, and stores structured metadata alongside raw file content in a local index. Each symbol includes enough information to be found cheaply and retrieved precisely later.

That includes metadata like:

• signature
• kind
• qualified name
• one-line summary
• byte offsets into the original file

So when the agent wants a symbol, jCodeMunch can fetch the exact source directly instead of loading and rescanning the full file.

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.

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 (PreToolUse read guard + PostToolUse auto-reindex + PreCompact session snapshot), optionally indexes your project, and audits your agent config files for token waste. Run jcodemunch-mcp init --help for all flags.

For non-interactive CI or scripting:

jcodemunch-mcp init --yes --claude-md global --hooks --index --audit

pip install jcodemunch-mcp

Want semantic search? Install the local embedding extra for zero-config semantic search — no API keys, no internet after first download:

pip install "jcodemunch-mcp[local-embed]"  # bundled ONNX encoder (recommended)
jcodemunch-mcp download-model              # fetch model (~23 MB, one-time)

Want AI-generated summaries? Install the extra for your provider:

pip install "jcodemunch-mcp[anthropic]"   # Claude
pip install "jcodemunch-mcp[gemini]"      # Gemini
pip install "jcodemunch-mcp[openai]"      # OpenAI-compatible
pip install "jcodemunch-mcp[all]"         # all providers + local embeddings

Without an extra, summaries fall back to signatures (which still works — you just get shorter descriptions). Run jcodemunch-mcp config --check to verify your provider is installed and working.

If you’re using Claude Code:

claude mcp add jcodemunch uvx jcodemunch-mcp

If you’re using Paperclip (the multi-agent orchestration platform), add a .mcp.json to your workspace root:

{
  "mcpServers": {
    "jcodemunch": {
      "type": "stdio",
      "command": "uvx",
      "args": ["jcodemunch-mcp"]
    },
    "jdocmunch": {
      "type": "stdio",
      "command": "uvx",
      "args": ["jdocmunch-mcp"]
    }
  }
}

Paperclip’s Claude Code agents auto-detect .mcp.json at startup. Add both servers to give your agents symbol search + doc navigation without blowing the token budget.

This matters more than people think.

Installing jCodeMunch makes the tools available. It does not guarantee the agent will stop its bad habit of brute-reading files unless you instruct it to prefer symbol search, outlines, and targeted retrieval. The changelog specifically calls out improved onboarding around this because it is a real source of confusion for first-time users.

A simple instruction like this helps:

Use jcodemunch-mcp for code lookup whenever available. Prefer symbol search, outlines, and targeted retrieval over reading full files.

Note: jcodemunch-mcp init handles steps 2 and 3 automatically. For a comprehensive guide on enforcing these rules through agent hooks and prompt policies, see AGENT_HOOKS.md.

Pre-built indexes for popular frameworks and libraries. Skip the initial indexing step — install a pack and start querying immediately.

# List available packs
jcodemunch-mcp install-pack --list

# Install a free pack
jcodemunch-mcp install-pack fastapi

# Install a licensed pack
jcodemunch-mcp install-pack express --license YOUR-KEY

Free packs require no license. Licensed packs require a jCodeMunch license. Use --force to re-download an already-installed pack.

Settings are controlled by a JSONC config file (config.jsonc) with env var fallbacks for backward compatibility. Defaults are chosen so that a fresh install works without any configuration.

jcodemunch-mcp config --init       # create ~/.code-index/config.jsonc from template
jcodemunch-mcp config              # show effective configuration
jcodemunch-mcp config --check      # validate config + verify prerequisites

--check validates that your config file is well-formed, your AI provider package is installed, your index storage path is writable, and HTTP transport packages are present. Exits non-zero on any failure — useful for CI/CD or first-run scripts.

Project config merges over global config — closest to the work wins.

See the full template for all available keys. Run jcodemunch-mcp config --init to generate one.

Place a .jcodemunch.jsonc file at your project root to declare the layers your architecture must respect. get_layer_violations will then enforce that imports only flow in the declared direction.

// .jcodemunch.jsonc — example for a layered Python project
{
  "architecture": {
    "layers": [
      { "name": "api",      "paths": ["src/routes", "src/controllers"] },
      { "name": "service",  "paths": ["src/services"] },
      { "name": "repo",     "paths": ["src/repositories"] },
      { "name": "db",       "paths": ["src/models", "src/migrations"] }
    ],
    "rules": [
      { "layer": "api",     "may_not_import": ["db"] },
      { "layer": "service", "may_not_import": ["api"] },
      { "layer": "repo",    "may_not_import": ["api", "service"] }
    ]
  }
}

Call get_layer_violations(rules=[...]) directly to pass rules inline — the config file is optional and used as a fallback. When no config is present, get_layer_violations infers layers from top-level directory structure.

The following env vars still work but are deprecated. Config file values take priority:

AI provider keys (ANTHROPIC_API_KEY, GOOGLE_API_KEY, OPENAI_API_BASE, MINIMAX_API_KEY, ZHIPUAI_API_KEY, etc.), JCODEMUNCH_SUMMARIZER_PROVIDER, and CODE_INDEX_PATH are always read from env vars — they are never placed in config files.

AI provider priority in auto-detect mode: Anthropic → Gemini → OpenAI-compatible (OPENAI_API_BASE) → MiniMax → GLM-5 → signature fallback. Set JCODEMUNCH_SUMMARIZER_PROVIDER to force anthropic, gemini, openai, minimax, glm, or none. jcodemunch-mcp config shows which provider is active.

allow_remote_summarizer only affects OpenAI-compatible HTTP endpoints. When false, jcodemunch accepts only localhost-style endpoints such as Ollama or LM Studio on 127.0.0.1 and rejects remote hosts like api.minimax.io. When a remote endpoint is rejected, AI summarization falls back to docstrings or signatures instead of sending source code to that provider. Set allow_remote_summarizer: true in config.jsonc if you intentionally want to use a hosted OpenAI-compatible provider such as MiniMax or GLM-5.

A common question: does this only help during exploration, or also when the agent is prompted to read a file before editing?

It helps most when editing a specific function. The "read before edit" constraint doesn't require reading the whole file — it requires reading the code. get_symbol_source gives you exactly the function body you're about to touch, nothing else. Instead of reading 700 lines to edit one method, you read those 30 lines.

The cases where it doesn't help: edits that genuinely require understanding the entire file (restructuring file-level state, reordering logic that spans hundreds of lines). For those, get_file_content is roughly equivalent to Read. The cases where it helps most are targeted edits — one function, one method, one class — which is the majority of real editing work.

• large repositories
• unfamiliar codebases
• agent-driven code exploration
• refactoring and impact analysis
• teams trying to cut AI token costs without making agents dumber
• developers who are tired of paying premium rates for glorified file scrolling

Start with QUICKSTART.md for the fastest setup path.

Then index a repo, ask your agent what it has indexed, and have it retrieve code by symbol instead of reading entire files. That is where the savings start.