qmd

name	qmd
description	Search local markdown knowledge bases, notes, docs, and wikis with QMD. Use when users ask to find notes, retrieve documents, inspect a wiki, answer from indexed markdown, or set up QMD access.
license	MIT
compatibility	Requires qmd CLI or MCP server. Install via `npm install -g @tobilu/qmd`.
metadata	{"author":"tobi","version":"2.2.0"}
allowed-tools	Bash(qmd:*), mcp__qmd__*

QMD - Query Markdown Documents

How search works

QMD searches local markdown collections: notes, docs, wikis, transcripts, and project knowledge bases. Use it before web search when the answer may already be in indexed local files.

The workflow is always:

1. Search for candidate documents.
2. Retrieve the full source with qmd get or qmd multi-get.
3. Answer from retrieved text, citing paths or docids.

Do not answer from snippets alone when the user needs facts, decisions, quotes, or nuance. Snippets are only leads.

Typical loop:

qmd search "merchant reality support interviews" -n 5
# leads: #abc123 concepts/customer-proximity.md; #def432 sources/merchant-call.md
qmd multi-get "#abc123,#def432" --format md

Default to structured qmd query with intent:, lex:, vec:, and hyde: fields that you write yourself. You are a better query expander than the built-in model: you know the user's actual goal, the domain vocabulary, and the nearby-but-wrong concepts to avoid. Do not just paste the user's words into qmd query "..." and hope the expansion model guesses right — supply the intent: and craft the lexical and semantic terms deliberately (see Pick the right search mode).

When reporting what you retrieved, a compact note is enough; do not paste whole files unless needed:

Retrieved:
- #abc123 concepts/customer-proximity.md
- #def432 sources/merchant-call.md

Pick the right search mode

Use BM25 lexical search when you know exact words, titles, names, code symbols, or rare phrases:

qmd search "cockpit OKR Goodhart" -n 10
qmd search '"AI Before Headcount"' -c concepts -n 5

Use qmd query with structured fields when the user describes an idea indirectly, uses different wording than the source, or needs conceptual recall. This is the default mode — write the fields yourself rather than leaning on query expansion. Combine exact anchors with semantic recall:

qmd query $'intent: Find the concept note about metrics as instruments without letting OKRs replace judgment.\nlex: cockpit instruments OKR Goodhart metrics judgment\nvec: data informed not metric driven product judgment\nhyde: A concept note says metrics are useful like cockpit instruments, but leaders should remain data-informed rather than metric-driven because OKRs and dashboards can Goodhart product judgment.'

Structured query fields (you author each one — do not delegate this to the expansion model):

• intent: states what you are trying to find and what to avoid. Always supply this. It steers ranking away from nearby-but-wrong concepts.
• lex: exact terms, aliases, titles, code symbols, and rare words you expect in the source. This is your own keyword expansion.
• vec: paraphrases the idea in natural language, in source-like wording.
• hyde: describes the document or answer that would satisfy the request.

You do not need all four every time, but you should almost always write at least intent: plus one of lex:/vec:. A bare qmd query "the user's sentence" throws away the context only you have and relies on the built-in expander to reconstruct it — prefer the structured form.

If you genuinely have nothing to expand (a single rare token, a verbatim phrase), that is a job for qmd search, not bare qmd query:

qmd query --format json --explain $'intent: ...\nlex: ...\nvec: ...'  # inspect ranking

If qmd query is slow or model/GPU setup fails, fall back to qmd search with better lexical terms.

Retrieve sources

Search results include docids like #abc123 and qmd://... paths. Fetch them:

qmd get "#abc123"
qmd get qmd://concepts/ai-before-headcount.md
qmd multi-get "#abc123,#def432" --format md
qmd multi-get 'concepts/{ai-before-headcount.md,data-informed-not-metric-driven.md}' --format md
qmd multi-get 'sources/podcast-2025-*.md' -l 80

Use multi-get when comparing several hits or gathering context across pages.

Output is line-numbered and carries the docid — cite both

get and multi-get are line-numbered by default and always print the document's #docid and qmd:// path. So get output looks like:

qmd://concepts/note.md  #abc123
---

1: # Metrics as instruments
2:
3: Treat dashboards like cockpit instruments...

Cite the docid and exact line numbers in your answer, and use the numbers to ask for the next slice. Pass --no-line-numbers only when you need raw content to copy verbatim (e.g. reproducing a code block).

When you need to open or edit the underlying file (e.g. hand a path to Read, Edit, or an editor), add --full-path. It replaces the qmd:// URL + docid header with the document's on-disk path, falling back to the canonical header if the file no longer exists on disk:

$ qmd get "#abc123" --full-path
/Users/you/notes/concepts/note.md
---

1: # Metrics as instruments

--full-path works the same way on qmd search and qmd query: result paths become the file's on-disk path — ./-prefixed relative path when the file is inside $PWD, absolute realpath otherwise — and the per-result #docid is dropped because the path is the identifier. The leading ./ is intentional so the output is unambiguously a filesystem path and cannot be mistaken for a bare collection-relative string. Default search/query output still uses qmd:// URIs; only opt into --full-path when you specifically need a path you can hand to a non-QMD tool.

Read line ranges with the :from:count suffix — never pipe through sed/head/tail

qmd get slices files itself. Use the suffix or flags; do not shell out to sed -n, head, tail, or awk to pull a line range. Piping defeats docid resolution, virtual-path lookups, line numbering, and the header, and it is slower and more error-prone.

The most compact form is a :from:count suffix right on the path or docid — prefer it:

qmd get "#abc123:120:40"                  # 40 lines starting at line 120
qmd get qmd://concepts/note.md:200:60     # lines 200–259
qmd get "#abc123:120"                      # from line 120 to end of file
qmd get "#abc123" --from 120 -l 40         # equivalent, using flags

Suffix and flags:

• <path>:<from>:<count> — start at line <from>, read <count> lines. Best for reading around a search hit.
• <path>:<from> — start at <from>, read to end of file.
• --from <line> / -l <lines> — flag equivalents. Explicit flags override the suffix, so ... :5:2 -l 1 reads 1 line.
• --no-line-numbers — drop the N: prefixes (line numbers are on by default).

Wrong: qmd get "#abc123" | sed -n '120,160p' Right: qmd get "#abc123:120:40"

Search results include a :line anchor on each hit — feed it straight into qmd get path:line:<n> to read a window around the match (line numbers in the output will start at line).

Discover what is indexed

qmd collection list
qmd ls
qmd status

Add collection filters when broad searches drift into the wrong corpus:

qmd search "headcount autonomous agents" -c concepts -n 10
qmd query "merchant support product reality" -c concepts -c sources -n 10

Omit -c to search everything.

MCP Tool: query

When using the MCP server, prefer structured searches:

{
  "searches": [
    { "type": "lex", "query": "cockpit OKR Goodhart" },
    { "type": "vec", "query": "data informed not metric driven product judgment" },
    {
      "type": "hyde",
      "query": "A concept note explains that metrics are useful as instruments, but leaders should not let OKRs or dashboards replace judgment."
    }
  ],
  "intent": "Find the concept note about using metrics as instruments without becoming metric-driven.",
  "collections": ["concepts"],
  "limit": 10
}

Query types:

• lex — BM25 keyword search. Best for exact terms, names, titles, and code.
• vec — vector semantic search. Best for natural-language concepts.
• hyde — vector search using a hypothetical answer/document passage.

Query craft

Good QMD searches mix three things:

1. Title/alias anchors: exact page titles, named entities, phrases.
2. Semantic paraphrase: how a human would describe the idea.
3. Negative space: enough intent to avoid nearby-but-wrong concepts.

Examples:

# Exact-ish title lookup
qmd search '"arm the rebels" merchants tools big companies' -c concepts

# Semantic concept lookup
qmd query $'intent: Find the customer proximity concept, not generic customer delight.\nlex: support pseudonymous merchant customer interviews\nvec: founder stays close to merchant reality through support and product use'

# Source lookup
qmd search "six-week cadence WhatsApp merchant relationships Shawn Ryan" -c sources -n 10

Setup and maintenance

Only mutate indexes when the user asked for setup or maintenance. Searching and retrieving are safe; collection/index mutation is not a casual first step.

npm install -g @tobilu/qmd
qmd collection add ~/notes --name notes
qmd update
qmd embed

Health and diagnostics:

qmd doctor
qmd status
qmd pull

qmd doctor checks config, model cache, device/GPU setup, vector fingerprints, and common environment overrides. If a model-backed command fails, run it before changing configuration.

MCP setup

See references/mcp-setup.md for Claude Code, Claude Desktop, OpenClaw, and HTTP server configuration.

Pitfalls

• Do not stop at snippets. Fetch documents before making claims.
• Do not slice files with sed/head/tail. Use the path:from:count suffix (e.g. qmd get "#abc123:120:40") or --from/-l. Output is already line-numbered; piping breaks docid resolution, the header, and virtual paths.
• Do not lean on query expansion. Write intent:/lex:/vec:/hyde: yourself. A bare qmd query "user sentence" discards the context only you have. You expand the query; the model just ranks.
• Do not overuse semantic search. If you know exact titles or terms, BM25 is faster and often better.
• Do not mutate indexes casually. qmd collection add, qmd update, and qmd embed change local state and can be expensive.
• Model-backed commands can be environment-sensitive. If qmd query, qmd vsearch, or reranking fails because local models/GPU are unavailable, use qmd search and stronger lexical/structured terms.
• Ambiguous user wording needs intent. Add intent: rather than hoping query expansion guesses the right domain.
• Collection names matter. Search concepts for synthesized wiki pages, sources for transcripts/raw source pages, and docs collections for code or project documentation.