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search_memory

Retrieve relevant passages from your personal knowledge base by asking natural-language questions. Results include file paths, relevance scores, and previews, with optional filtering by source or modality.

Instructions

Search the personal knowledge base for passages relevant to query.

    Returns a ranked list of matching text passages with file paths,
    relevance scores, and short previews.  When ``search.parent_window_chars``
    is configured, each hit also includes an ``extended_preview`` with wider
    context around the matched chunk.

    Args:
        query: Natural-language question or phrase.
        top_k: Number of results (clamped to 1-50).
        mode: ``hybrid`` | ``dense`` | ``sparse``.
        source: Optional source name filter.
        modality: ``all`` | ``text`` | ``image``.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo
modeNohybrid
sourceNo
modalityNoall
Behavior3/5

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

With no annotations, the description reveals that the tool returns a ranked list of passages with file paths, scores, and previews, plus an optional extended preview. It does not explicitly state read-only nature or side effects, but the behavior is clear for a search tool.

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

Conciseness5/5

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

The description is concise with a brief summary followed by a bulleted Args list. Every sentence adds value, no redundancy, and the purpose is front-loaded.

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

Completeness3/5

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

The description adequately covers inputs, outputs, and a config-dependent behavior. However, it lacks details on error handling, empty results, or how this tool fits among siblings, making it somewhat incomplete for full context.

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?

Schema coverage is 0%, but the description explains each parameter's purpose and possible values (e.g., query is a natural-language question, top_k clamped 1-50, mode options). It adds meaningful semantics beyond the bare schema.

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 a personal knowledge base for passages matching a query, listing returned elements. While it is distinct from siblings like query_timeline or search_by_date, it does not explicitly differentiate itself, hence not a 5.

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?

The description only explains what the tool does, with no guidance on when to use it vs alternatives like ask_memory or search_by_date. No when-not-to-use or context for selection.

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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