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/.

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.

pip install jdocmunch-mcp
jdocmunch-mcp init

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.

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

Verify:

jdocmunch-mcp --version

Manual Claude Code setup:

pip install jdocmunch-mcp
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 local sentence-transformers. 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.

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](https://github.com/jgravelle/jcodem