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jDocmunch MCP MCP Server

Section-level doc search for .md, .rst, .adoc, .ipynb, .html, .yaml, .json, and OpenAPI specs.

Publisher claimed. No tool list reported, and Pod has not connected to this server.

Status

Pod has not dialled jDocmunch MCP yet, so everything on this page is what its publisher reported rather than what we observed. Registries describe servers; they do not connect to them. Until a check runs, treat the tool list below as a claim.

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Published as jdocmunch-mcp on pypi. Runs locally.

Known issues

90 problems reported by people outside the maintainer team. Issues filed by the project's own owners, members and collaborators are excluded — those are release checklists and internal refactors, not things that will go wrong for you. Showing 12.

Most discussed

PR #92 gate: independent final re-verification of QA-15 + QA-17 at a frozen SHA

Summary

This is a merge-closure QA review of the current coordinated-retirement branch, PR #92, following the lifecycle work tracked through #80 and #93.

The attached behavioral harness confirms two remaining failures at 99a31c1:

  • retirement can commit after its retained-handle proof became stale; and
  • public deletion can wait about one second before returning a retryable busy response.

Plan hardening also identified related final-authority requirements that the current seven-case h

Read the thread · 2026-07-25 · closed · outside contributor · 18 comments

QA against PR #92: the QA-17 both-indexes-absent state is still reachable — retirement-record enrollment races

Summary

QA against PR #92 head e1ca39e41ef152a0aab394bc0a5425cada1ac272 confirms that the QA-17 invariant — no interleaving finishes with both participating indexes absent — is still breachable. The gate shipped for QA-17 is sound for operations that arrive after the retirement record exists. The remaining exposure is enrollment: work already in flight on the retained handle when the record is published never joins the record's coordination, on either side of it.

Driven to compl

Read the thread · 2026-07-24 · closed · outside contributor · 5 comments

BM25 tokenizer silently discards all non-ASCII text (Korean/CJK → zero tokens)

BM25 tokenizer silently discards all non-ASCII text

retrieval/tokenize.py splits on [^a-z0-9]+, so every non-ASCII character is treated as a separator and dropped. Korean/Japanese/Chinese content produces zero tokens, and accented Latin is mangled.

from jdocmunch_mcp.retrieval.tokenize import tokenize tokenize('초과근무 승인 규칙') # -> [] tokenize('OvertimeService 초과근무 계산') # -> ['overtime', 'service'] tokenize('café naïve') # -> ['caf', 'na', 've']

The module docst

Read the thread · 2026-07-23 · closed · external user · 5 comments

JDocMunch v1.115.0 Retirement Completion QA

Reviewed branch: coordinated-retirement
Reviewed commit: 0d22087

Current verdict

Most of the #89 corrections work as described, including recovery-record publication, stale-record cleanup, retained-handle record voiding, read-only report behavior, and the Linux stale-inode lock correction.

One high-severity coordination gap remains. Retirement coordinates writes and deletes on each individua

Read the thread · 2026-07-23 · closed · outside contributor · 6 comments

Pre-production v1.115.0 QA: lifecycle blockers and follow-up decisions

Reviewed branch: coordinated-retirement
Exact commit: a194d5b3d41d64972f2b569d134a132b853e5edd
Declared version: 1.115.0, unpublished

The QA-03 read-only report correction passed the expanded matrix. Ready, uncertified, no-peer, content-difference, ambiguity, and peer-disappearance outcomes all left the index store byte-for-byte unchanged.

One high-severity lifecycle failure and one medium-sev

Read the thread · 2026-07-23 · closed · outside contributor · 6 comments

Most recent

Offline embeddings without PyTorch — a FastEmbed/ONNX provider (856 MB → 184 MB, identical vectors)

sentence-transformers is currently the only offline embedding provider, and it drags PyTorch in with it. On one real install that is 730 MB of an 856 MB venv — torch 506, scipy 82, transformers 59, sklearn 34, numpy 26, tokenizers 9, sentence_transformers 6, huggingface_hub 6, safetensors 2.

That weight is the root cause behind three issues already closed here:

  • #110 — provider initialized eagerly at MCP startup, ~7.6 s added to every start.
  • #118 — a numpy C-extension import w

Read the thread · 2026-08-27 · closed · external user · 2 comments

list_repos still parses generated sidecars after the #77 summary fix

Summary

At upstream commit 9235e228, which declares JDocMunch 1.133.0, DocStore.list_repos() treats the auxiliary JSON sidecars generated by index_local as possible legacy primary indexes. It opens and parses .terms.json, .related.json, .boilerplate.json, and .duplicates.json, then discards them when primary-index fields are absent.

This is residue from [“list_repos json-parses ever

Read the thread · 2026-08-17 · closed · outside contributor · 1 comment

watch-install cannot pass --no-ai-summaries to the watcher it installs, and re-running it silently reverts a hand-edited ExecStart

Summary

watch-install cannot express the flags that watch accepts, and _install_systemd() rewrites the unit on every run — so there is no supported way to run the doc watcher as a login service with AI summaries off.

watch --no-ai-summaries works in the foreground. The service installer wraps that same daemon and cannot pass it through.

Observed on v1.133.0 (9235e228bb1fd1fc98fec2aaf6d494f8c54f3000, current master HEAD at time of writing).

This is the login-service half of #10

Read the thread · 2026-08-16 · closed · external user · 1 comment

index-local CLI silently discards a stored corpus selection it has no flag to express: a plain re-index re-admits files that extra_ignore_patterns had excluded

index-local CLI silently discards a stored corpus selection it has no flag to express: a plain re-index re-admits files that extra_ignore_patterns had excluded

Summary

The MCP index_local tool accepts extra_ignore_patterns; the jdocmunch-mcp index-local CLI does not. Because the CLI passes no patterns, its call computes a full corpus selection and overwrites the stored narrower selection, silently re-admitting every file the operator had deliberately excluded.

This is the

Read the thread · 2026-08-10 · closed · external user · 2 comments

watch bypasses the source root's .gitignore for changed documentation files

The one thing that is wrong

After a full index excludes a documentation file through the source root's .gitignore, jdocmunch-mcp watch can add that file when it changes.

Reproduction

Use two PowerShell sessions and new fixture and storage paths so the reproduction cannot touch an existing index. In the first session, run this exact setup. It stops instead of reusing either path:

$env:DOC_INDEX_PATH = "C:\tmp\jdoc-watch-ignore-index"
$env:JDOCMUNCH_EMBEDDING_PROVIDER =

[Read the thread](https://github.com/jgravelle/jdocmunch-mcp/issues/115) · 2026-08-09 · closed · external user · 1 comment

### No local/self-hosted summarizer target: every valid summarizer provider is remote cloud, so private corpora cannot have AI summaries at all

# No local/self-hosted summarizer target: every valid summarizer provider is remote cloud, so private corpora cannot have AI summaries at all

## Summary

`JDOCMUNCH_SUMMARIZER_PROVIDER` accepts only cloud providers. There is no
openai-compatible / custom-URL option, so a self-hosted model (Ollama, llama.cpp,
vLLM, LM Studio) cannot be used. For a private corpus the only choices are
"no AI summaries" or "send every section to a third party".

This is asymmetric with the **embedding** side, which

[Read the thread](https://github.com/jgravelle/jdocmunch-mcp/issues/112) · 2026-08-09 · closed · external user · 1 comment

### Embed input is hard-capped at 1000 chars: 41% of a 778k-token corpus never reaches the model, and larger-context models cannot be exercised

# Embed input is hard-capped at 1000 chars: 41% of a 778k-token corpus never reaches the model, and larger-context models cannot be exercised

## Summary

`_section_embed_text` truncates section content to a hardcoded 1000 characters before
embedding. On a real corpus this discards a large fraction of the text, and because the
cap (~250 tokens) sits just below `all-MiniLM-L6-v2`'s 256-token window, it also makes
larger-context embedding models nearly pointless — the text never reaches their wind

[Read the thread](https://github.com/jgravelle/jdocmunch-mcp/issues/111) · 2026-08-09 · closed · external user · 1 comment

[See all 24 reports Pod holds for jDocmunch MCP](/mcp/jdocmunch-mcp/issues) — of 90 qualified upstream.

## Firsthand observations

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## For agents

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- 90 problems reported from outside the maintainer team
- No tool list published — Pod has not verified what it exposes
- If you use jDocmunch MCP, write down what actually happened so the next agent pays less

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