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ruliana

mcp-pkm-logseq

by ruliana

get_personal_notes

Retrieve tagged personal notes from your Logseq knowledge base by topic or date range. Returns markdown with linked topics for further exploration.

Instructions

Retrieve personal notes from Logseq.

Use this to find relavant information about a specific topic or about user preferences.
It will return all information that is tagged with the topics from the user's personal
knowledge base.

The information is returned in markdown format, each item in the list is a separate
unit of information. Hierachical information is returned as a nested list.

The returning markdown contains text in double square brackets, like this:
`[[topic]]`. These are links to other topics, you can follow them to get more
information. Try variations of the topic to find the most relevant information.

Dates are expressed as:
1. today|yesterday|tomorrow|now
2. <number><unit> like 1d, 2w, 3m, 4y, 2h, 30min

Args:
    topics: A list of topics to search for. Topics are case-insensitive. Topics
            are optional if there is a date range.
    from_date (optional): The start date to filter the notes.
    to_date (optional): The end date to filter the notes.

Returns:
    A markdown formatted string containing the information with the given topics.
    Empty if no information is found.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicsYes
from_dateNo
to_dateNo
Behavior4/5

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

Despite no annotations, the description discloses return format (markdown, nested lists, double-square-bracket links), date format, and empty returns. It lacks details on permissions or rate limits but adequately covers behavioral aspects for a read tool.

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

Conciseness4/5

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

The description is well-structured with paragraphs and an Args section, but the inclusion of markdown link and date format details, while useful, makes it slightly verbose. Overall, it earns its length.

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

Completeness5/5

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

Given no output schema, the description fully explains the return format (markdown, nested list, links) and empty results. All three parameters are documented, and sibling tools are distinguished. It provides sufficient context for correct invocation.

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

Parameters4/5

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

The description adds meaning beyond the schema by explaining that topics are case-insensitive and optional with date ranges, and provides date format examples. This compensates for the schema's minimal descriptions, though it could include more on parameter constraints.

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

Purpose5/5

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

The description clearly states 'Retrieve personal notes from Logseq' and distinguishes from siblings like get_todo_list and get_personal_notes_instructions by focusing on topic-based retrieval of notes.

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

Usage Guidelines4/5

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

The description specifies use for finding relevant information on a topic or user preferences and notes that topics are optional if a date range is given. It does not explicitly exclude use cases but provides clear context for when to employ the tool.

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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