qmd

name	qmd
description	Search markdown knowledge bases, notes, and documentation using QMD. Use when users ask to search notes, find documents, or look up information.
license	MIT
compatibility	Requires qmd CLI or MCP server. Install via `npm install -g @tobilu/qmd`.
metadata	{"author":"tobi","version":"2.0.0"}
allowed-tools	Bash(qmd:*), mcp__qmd__*

QMD - Quick Markdown Search

Local search engine for markdown content.

Status

!qmd status 2>/dev/null || echo "Not installed: npm install -g @tobilu/qmd"

MCP: query

{
  "searches": [
    { "type": "lex", "query": "CAP theorem consistency" },
    { "type": "vec", "query": "tradeoff between consistency and availability" }
  ],
  "collections": ["docs"],
  "limit": 10
}

Query Types

Type	Method	Input
lex	BM25	Keywords - exact terms, names, code
vec	Vector	Question - natural language
hyde	Vector	Answer - hypothetical result (50-100 words)

Writing Good Queries

lex (keyword)

• 2-5 terms, no filler words
• Exact phrase: "connection pool" (quoted)
• Exclude terms: performance -sports (minus prefix)
• Code identifiers work: handleError async

vec (semantic)

• Full natural language question
• Be specific: "how does the rate limiter handle burst traffic"
• Include context: "in the payment service, how are refunds processed"

hyde (hypothetical document)

• Write 50-100 words of what the answer looks like
• Use the vocabulary you expect in the result

expand (auto-expand)

• Use a single-line query (implicit) or expand: question on its own line
• Lets the local LLM generate lex/vec/hyde variations
• Do not mix expand: with other typed lines - it's either a standalone expand query or a full query document

Intent (Disambiguation)

When a query term is ambiguous, add intent to steer results:

{
  "searches": [
    { "type": "lex", "query": "performance" }
  ],
  "intent": "web page load times and Core Web Vitals"
}

Intent affects expansion, reranking, chunk selection, and snippet extraction. It does not search on its own - it's a steering signal that disambiguates queries like "performance" (web-perf vs team health vs fitness).

Combining Types

Goal	Approach
Know exact terms	lex only
Don't know vocabulary	Use a single-line query (implicit expand:) or vec
Best recall	lex + vec
Complex topic	lex + vec + hyde
Ambiguous query	Add intent to any combination above

First query gets 2x weight in fusion - put your best guess first.

Lex Query Syntax

Syntax	Meaning	Example
term	Prefix match	perf matches "performance"
"phrase"	Exact phrase	"rate limiter"
-term	Exclude	performance -sports

Note: -term only works in lex queries, not vec/hyde.

Collection Filtering

{ "collections": ["docs"] }              // Single
{ "collections": ["docs", "notes"] }     // Multiple (OR)

Omit to search all collections.

Other MCP Tools

Tool	Use
get	Retrieve doc by path or #docid
multi_get	Retrieve multiple by glob/list
status	Collections and health

CLI

qmd query "question"              # Auto-expand + rerank
qmd query $'lex: X\nvec: Y'       # Structured
qmd query $'expand: question'     # Explicit expand
qmd query --json --explain "q"    # Show score traces (RRF + rerank blend)
qmd search "keywords"             # BM25 only (no LLM)
qmd get "#abc123"                 # By docid
qmd multi-get "journals/2026-*.md" -l 40  # Batch pull snippets by glob
qmd multi-get notes/foo.md,notes/bar.md   # Comma-separated list, preserves order

HTTP API

curl -X POST http://localhost:8181/query \
  -H "Content-Type: application/json" \
  -d '{"searches": [{"type": "lex", "query": "test"}]}'

Setup

npm install -g @tobilu/qmd
qmd collection add ~/path/to/markdown --name myknowledge
qmd embed