Search Query Language

Hoarder provides a search query language to filter and find bookmarks. Here are all the supported qualifiers and how to use them:

Basic Syntax

• Use spaces to separate multiple conditions (implicit AND)
• Use and/or keywords for explicit boolean logic
• Use parentheses () for grouping conditions
• Prefix qualifiers with - to negate them

Qualifiers

Here's a comprehensive table of all supported qualifiers:

Qualifier	Description	Example Usage
is:fav	Favorited bookmarks	is:fav
is:archived	Archived bookmarks	-is:archived
is:tagged	Bookmarks that has one or more tags	is:tagged
is:inlist	Bookmarks that are in one or more lists	is:inlist
url:<value>	Match bookmarks with URL substring	url:example.com
#<tag>	Match bookmarks with specific tag	#important
	Supports quoted strings for tags with spaces	#"work in progress"
list:<name>	Match bookmarks in specific list	list:reading
	Supports quoted strings for list names with spaces	list:"to review"
after:<date>	Bookmarks created on or after date (YYYY-MM-DD)	after:2023-01-01
before:<date>	Bookmarks created on orbefore date (YYYY-MM-DD)	before:2023-12-31

Examples

# Find favorited bookmarks from 2023 that are tagged "important"
is:fav after:2023-01-01 before:2023-12-31 #important

# Find archived bookmarks that are either in "reading" list or tagged "work"
is:archived and (list:reading or #work)

# Find bookmarks that are not tagged or not in any list
-is:tagged or -is:inlist

Combining Conditions

You can combine multiple conditions using boolean logic:

# Find favorited bookmarks from 2023 that are tagged "important"
is:fav after:2023-01-01 before:2023-12-31 #important

# Find archived bookmarks that are either in "reading" list or tagged "work"
is:archived and (list:reading or #work)

# Find bookmarks that are not favorited and not archived
-is:fav -is:archived

Text Search

Any text not part of a qualifier will be treated as a full-text search:

# Search for "machine learning" in bookmark content
machine learning

# Combine text search with qualifiers
machine learning is:fav