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tomschell
by tomschell

kg_search

Locate knowledge nodes using text queries, AI-based meaning search, or date filters. Get ranked results with tag and type options.

Instructions

Unified search tool for finding knowledge nodes. Supports three modes: 'text' for keyword/tag filtering with ranking, 'semantic' for AI-powered meaning-based search, 'time_range' for date-based queries. Default mode is 'semantic' for best results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo[time_range mode] End date (e.g., '2024-12-31', 'today').
modeNoSearch mode: 'text' for keyword/tag search with ranking, 'semantic' for AI-powered similarity search, 'time_range' for date-based search.semantic
tagsNo[text mode] Filter by tags. Tags are expanded with synonyms and hierarchies.
typeNo[text mode] Filter by node type: idea, decision, progress, insight, question, session.
limitNoMaximum number of results to return.
queryNoSearch query text. Required for 'text' and 'semantic' modes. For 'semantic' mode, be descriptive - it finds conceptually similar content.
startNo[time_range mode] Start date (e.g., '2024-01-01', '2 weeks ago').
formatNo[text mode] Output format for results.
hybridNo[semantic mode] Combine semantic similarity with tag matching and term presence.
thresholdNo[semantic mode] Minimum similarity score (0-1). Higher = stricter matching.
includeTagsNo[text mode] Include tags in results.
includeContentNo[text mode] Include full content in results.
includeMetadataNo[text mode] Include metadata in results.
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral disclosure burden. It does describe the behavioral characteristics of each mode (e.g., 'keyword/tag filtering with ranking', 'AI-powered meaning-based search', 'date-based queries'), which adds some value. But it does not disclose return format, pagination, error behavior, or explicitly state read-only nature. This is adequate but not rich.

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 two compact sentences, front-loading the core purpose ('Unified search tool') and then efficiently summarizing the three modes and default recommendation. Every word earns its place; no fluff or redundancy.

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?

Given the tool's complexity (13 parameters, mode-dependent behavior, no output schema), the description is somewhat underspecified. It explains the modes at a high level but does not provide guidance on selecting between modes for common use cases, expected result structure, or interaction with sibling tools. It is not grossly incomplete, but it leaves meaningful gaps.

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 description coverage is 100%, so the baseline is 3. The description adds value by explaining the high-level mode concept and default behavior, which helps users understand how the mode-dependent parameters (like query, tags, start/end) relate to each other. This goes beyond the individual parameter schemas.

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 the tool's function with a specific verb ('search'), the resource ('knowledge nodes'), and differentiates itself from siblings by calling itself 'Unified search tool' and listing three distinct modes (text, semantic, time_range). This makes it immediately distinguishable from other kg_* tools.

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 provides clear context on when to use the tool: it is the 'unified search tool' for finding knowledge nodes, with a default mode recommendation ('semantic' for best results). However, it does not explicitly mention alternatives or exclusions relative to sibling tools like kg_get_relevant_context or kg_query_context.

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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