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karakeep-app

Karakeep MCP server

by karakeep-app

search-bookmarks

Search bookmarks by full-text query and qualifiers like is:fav, is:archived, tags, lists, and date ranges.

Instructions

Search for bookmarks matching a specific a query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoThe number of results to return in a single query.
queryYes By default, this will do a full-text search, but you can also use qualifiers to filter the results. You can search bookmarks using specific qualifiers. is:fav finds favorited bookmarks, is:archived searches archived bookmarks, is:tagged finds those with tags, is:inlist finds those in lists, and is:link, is:text, and is:media filter by bookmark type. url:<value> searches for URL substrings, #<tag> searches for bookmarks with a specific tag, list:<name> searches for bookmarks in a specific list given its name (without the icon), after:<date> finds bookmarks created on or after a date (YYYY-MM-DD), and before:<date> finds bookmarks created on or before a date (YYYY-MM-DD). If you need to pass names with spaces, you can quote them with double quotes. If you want to negate a qualifier, prefix it with a minus sign. ## 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) ### Combine text search with qualifiers machine learning is:fav
nextCursorNoThe next cursor to use for pagination. The value for this is returned from a previous call to this tool.
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, and the description does not disclose any behavioral traits such as read-only nature, pagination handling, or rate limits. It only repeats the basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (one sentence) with a typo ('a query'). It is concise but lacks important structural elements like key behavioral notes or examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite good schema coverage, the description omits return value format and pagination details. Given no output schema, the description should explain what the result list contains.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the detailed schema descriptions already explain the parameters. The tool description adds no additional semantic value beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches bookmarks with a query, which distinguishes it from other bookmarks tools that retrieve specific items or lists. However, it could be more specific about the scope (e.g., full-text vs. filtered).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus sibling tools like get-tag-bookmarks or get-bookmark. The description does not mention alternatives or context for appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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