jDocMunch MCP

jDocMunch is an MCP server for coding agents that retrieves the exact documentation section a task needs, without loading whole files into the context window.

Index a documentation set once by heading hierarchy, then fetch a single section, a heading subtree, or a ranked search result — extracted byte-precisely from the original file.

Install · Quickstart · Benchmarks · Commercial licensing

Free for personal use. Commercial use requires a paid license — terms below.

---

Why jDocMunch?

The problem. An agent asked "how do I configure authentication?" opens a documentation file, skims hundreds of paragraphs it does not need, opens another, and repeats. Large context windows do not fix this. They just make the waste affordable enough to ignore until the bill arrives, and they crowd out the context the model actually needed.

The mechanism. jDocMunch parses a documentation set into a section tree keyed by heading hierarchy, stores each section's byte offsets into the original file, and exposes retrieval over MCP. Sections keep durable identities across re-indexing as long as path, heading text, and heading level are unchanged.

The outcome. The unit of access changes from file to section. An agent retrieves the installation section, one configuration block, or a specific heading subtree — and nothing else.

---

What makes it different

Section-first retrieval

Search and retrieve documentation by section, not just file path or keyword match.

Byte-precise extraction

Full content is pulled on demand from exact byte offsets into the original file.

Stable section IDs

Sections retain durable identities across re-indexing when path, heading text, and heading level remain unchanged.

---

Evidence

Four benchmarks against public documentation corpora, each with the corpus, date, and per-query results recorded in benchmarks/.

Corpus	Scale	Indexed in	Result
Kubernetes (kubernetes/website, 2026-03-04)	1,569 .md files, 4,355 sections, 16 MB	3,352 ms	27,285 tokens saved on a single node-affinity query; 100 ms latency
SciPy	10,402 sections, ~855,000 corpus tokens	2,247 ms	135–152 ms per query across sparse-solver, FFT, and optimization lookups
LangChain (MDX)	5,973 sections	5,204 ms	MDX-aware sectioning found 754% more sections than the naive pass
Wiki	7,449-token corpus	—	Search returns ranked metadata in ~190 tokens against a 7,449-token whole-corpus read

Read these as per-corpus results, not as a single headline multiple. Savings depend on how large the containing file is relative to the section you needed: a small file with one heading saves almost nothing, and the Kubernetes corpus saves a great deal. The benchmark files record the queries that did poorly alongside the ones that did well.

A separate, measured result from the v1.121.0 projection work, on this repository's own docs at max_results=10: a search row went 1,989 chars → 319 with compact=true (−84%), or 431 with snippet_bytes=200 (−78%) while removing the follow-up get_section call entirely.

Retrieval quality is gated, not assumed. Every release runs a replay fixture over a frozen golden set and fails below nDCG 0.95. That gate has failed builds and blocked releases; it is not decorative.

---

Install

Requirements: Python 3.10+, any MCP-compatible client.

uv tool install jdocmunch-mcp
jdocmunch-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 detects your MCP clients, writes their config entries, installs the doc-exploration prompt policy so your agent actually reaches for the tools, and optionally installs hooks and indexes your docs.

Other install paths

Command	Use it when
uvx jdocmunch-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. ⚠ 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 jdocmunch-mcp	You already standardise on pipx
pip install jdocmunch-mcp	Inside a virtualenv you manage yourself

Verify:

jdocmunch-mcp --version

Manual Claude Code setup:

claude mcp add -s user jdocmunch -- uvx jdocmunch-mcp

No install step — uvx fetches and runs the server on demand. Prefer it on your PATH (and required for hooks)? uv tool install jdocmunch-mcp, then claude mcp add -s user jdocmunch jdocmunch-mcp.

Installing the server makes the tools available; it does not break an agent's habit of brute-reading files. One line in your CLAUDE.md does that:

Call the jdocmunch_guide tool and strictly follow its instructions.

---

Quickstart

Assumes: jDocMunch installed and registered with your client, and a folder of documentation.

Index a local documentation folder:

jdocmunch-mcp index-local --path ./docs

It prints JSON naming the corpus and what it found:

{
  "success": true,
  "repo": "local/docs",
  "file_count": 1,
  "section_count": 4,
  "doc_types": { ".md": 1 },
  "semantic_search": false
}

section_count greater than file_count is the whole point: the index addresses headings, not files.

Then, inside your agent:

Using jdocmunch, search the docs for "authentication configuration" and show me that section.

The agent should call search_sections, then get_section on the top hit — returning one section rather than a file. _meta.tokens_saved on the response reports what that cost versus reading the containing document.

Next step: get_toc_tree for a structural view of the whole corpus, or index_repo to index documentation straight from a GitHub repository.

---

What you can do

• Retrieve one section instead of a document. get_section and get_sections pull byte-precise content from the original file; get_section_excerpt narrows further.
• Search by meaning, not just keywords. search_sections fuses BM25 with semantic cosine when an embedding provider is configured. compact=true, fields=[...], and snippet_bytes=N cut the response further.
• Navigate structure. get_toc, get_toc_tree, get_section_path, get_section_descendants, and section_neighbors traverse the heading tree without reading content.
• Find what documentation is missing or rotting. get_doc_coverage, get_undocumented_symbols, get_stale_pages, get_orphan_sections, get_broken_links, and doc_health_radar.
• Work across API specs. find_endpoint, list_endpoints_by_tag, find_operations_using_schema, and get_schema_graph treat OpenAPI documents as first-class.
• Preflight documentation changes. check_section_delete_safe and get_section_blast_radius before you remove or restructure.
• Know when an answer is stale. Content reads disclose _meta.freshness, _meta.verdict, and which source layer answered.

64 tools in total. The full reference is in USER_GUIDE.md.

---

How it works

Everything runs locally. Indexes live under your home directory; no hosted service is required for indexing or retrieval.

docs/ ──► parser (per format) ──► section tree ──► local index
                                                      │
                          MCP client ◄── retrieval ◄──┘

• Parsing is per format, one module each: Markdown/MDX, reStructuredText, AsciiDoc, Jupyter notebooks, HTML, plain text, OpenAPI (YAML), JSON/JSONC, XML/SVG/XHTML, Godot scenes, and — via the optional [office] extra — PDF, DOCX, PPTX, and EPUB.
• Storage is a versioned local index (INDEX_VERSION = 3) that auto-migrates on first load. A 1.x release never forces a reindex.
• Retrieval is lexical BM25 by default, hybrid when embeddings are available.
• Embeddings are optional and provider-agnostic — Gemini, OpenAI, an OpenAI-compatible endpoint, or a local offline model through either FastEmbed (ONNX) or sentence-transformers (torch). Without one, search stays lexical and entirely offline.

Deeper detail: ARCHITECTURE.md and SPEC.md.

---

Security and privacy

Local-first by design. Your documentation is parsed and stored on your machine, and the base package's only default network behavior is an anonymous savings counter — a random ID plus aggregate token counts, no content, no paths, no PII.

Opt out completely:

JDOCMUNCH_SHARE_SAVINGS=0

Embedding and summarizer providers call their configured API only when you enable them, and never by default. watch-install registers a login service only when you run it yourself.

Background behavior, fully disclosed

A model download, the first time a local embedding provider runs. Both offline providers fetch their model from HuggingFace on first use and cache it on disk. Nothing downloads until you enable embeddings, and a lexical-only install never contacts the hub. Startup warmup is skipped when the model is not already cached, so a first run defers the download to your first search rather than stalling the MCP handshake behind it (#110).

FastEmbed as the offline provider. pip install jdocmunch-mcp[fastembed] runs the same all-MiniLM-L6-v2 model through onnxruntime instead of torch, which is a much smaller install. When both offline providers are present FastEmbed is preferred; JDOCMUNCH_EMBEDDING_PROVIDER=sentence-transformers selects the other one. On the shared model the two write the same vector store, so switching runtimes does not re-embed your corpus. Point FastEmbed at a different model with JDOCMUNCH_FASTEMBED_MODEL and it keeps its own vectors instead, because vectors from two models are not interchangeable (#126).

A child process, when local embeddings are in use. When the sentence-transformers provider is active, jDocMunch runs the embedding model in a child process (python -m jdocmunch_mcp.embeddings.worker) instead of inside the server. It:

• starts when something first needs an embedding — at startup if the model is already in your local HuggingFace cache, otherwise on the first search or index that uses it. A lexical-only install never spawns it;
• opens no network connection and speaks only to its parent, over a private pipe;
• exits when the server exits, and is killed if it stops responding;
• is not a login service, is not registered anywhere, and survives nothing.

This exists because importing the embedding stack inside the server process can deadlock in the Windows loader (#118), hanging every tool call for as long as the server runs. Disable it with JDOCMUNCH_EMBED_WORKER=0, which restores the previous in-process import.

A login service, only if you install one. jdocmunch-mcp watch-install registers the doc watcher to start at login (systemd user unit, launchd agent, or a Task Scheduler task named jdocmunch-watch). Nothing installs it for you. Once installed it:

• re-indexes every locally-indexed doc repo when a doc file on disk changes;
• runs exactly jdocmunch-mcp watch with the flags you passed to watch-install — --no-ai-summaries to keep the summarizer out of it, --quiet to suppress its per-change log lines;
• writes to watch.log and watch.err under your doc-index directory;
• is removed by jdocmunch-mcp watch-uninstall.

⚠ Re-running watch-install rewrites the service definition, so a hand-edited one is replaced. It now prints what it replaced; pass the flags to watch-install itself so an upgrade keeps them (#120).

Path traversal prevention, symlink escape protection, secret exclusion, file-size limits, binary detection, and encoding safety are documented in SECURITY.md, along with how to report a vulnerability.

---

Limitations

• Section retrieval helps least on small files. If a document has one heading and 40 lines, retrieving the section and reading the file cost about the same.
• Semantic search requires an embedding provider. Without one, search is lexical only — good for identifiers and exact phrasing, weaker for paraphrased questions.
• Office formats need the optional [office] extra and are supported for local indexing only.
• Freshness is disclosed, not guaranteed. A section whose source cannot be checked is reported as unknown rather than assumed current.
• jDocMunch does not parse code. Symbols, signatures, and call graphs belong to jcodemunch-mcp; tabular data belongs to jdatamunch-mcp.

---

Documentation

Doc	What it covers
USER_GUIDE.md	Full tool reference, workflows, and best practices
ARCHITECTURE.md	Storage model, parsing pipeline, extension points
SPEC.md	Response contracts and reason-code vocabulary
SECURITY.md	Security controls and vulnerability reporting
TOKEN_SAVINGS.md	How savings are counted and reported
CONTRIBUTING.md	Development setup and the CLA requirement
CHANGELOG.md · ROADMAP.md	Release history and what's next

---

Licensing and commercial use

Released under the jDocMunch-MCP Dual-Use License (full terms). Free for non-commercial use. Commercial use requires a paid license, one-time, sold by jMunch LLC.

jDocMunch only: Builder, $29 (1 developer) · Studio, $99 (up to 5) · Platform, $499 (org-wide internal deployment)

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

Individual developers and non-commercial projects need no license. Organizations deploying jDocMunch across internal teams do.

1.x compatibility commitment

Every 1.x license entitles you to every future 1.x release. We will never ship a 1.x version that:

• removes or renames an MCP tool (deprecated tool names keep their aliases),
• drops a Section field from the response shape,
• forces a reindex without auto-migrating your existing index on first load,
• changes the JSON wire format of any tool response in a way that breaks an existing consumer,
• or makes a previously-default behavior raise.

Anything that would require breaking these promises is reserved for a future major version (2.x). The full machine-checked contract is enforced via tests/test_server.py (tool-name and required-field invariants) and the replay-fixture gate that runs on every release.

---

Support and project status

Actively maintained. Issues and bug reports: GitHub Issues. Security reports: see SECURITY.md. Commercial licensing questions go through jcodemunch.com.

Part of the jMunch suite alongside jcodemunch-mcp (code symbols) and jdatamunch-mcp (tabular data). All three implement jMRI, the open retrieval interface spec.