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jmeyer1980

neurodivergent-memory

search_memories

Search and rank memories by semantic relevance with optional filters for district, project, tags, emotional valence, and intensity.

Instructions

Search memories using BM25 semantic ranking with optional goal-context blending, recency bias, and filters. Returns results sorted by relevance score (0-1).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query
districtNoOptional district filter
project_idNoOptional project_id filter
tagsNoOptional tag filters (OR logic)
epistemic_statusesNoOptional epistemic status filters
min_scoreNoMinimum relevance score (0-1). Only return results at or above this threshold.
contextNoOptional short goal/context string blended into ranking as a lightweight BM25 boost.
recency_weightNoOptional recency boost strength from 0 (off) to 1 (strongest). Recent memories receive more weight without replacing semantic relevance.
emotional_valence_minNoMinimum emotional valence filter (-1 to 1)
emotional_valence_maxNoMaximum emotional valence filter (-1 to 1)
intensity_minNoMinimum intensity filter (0-1). Deprecated alias for min_intensity.
intensity_maxNoMaximum intensity filter (0-1). Deprecated alias for max_intensity.
min_intensityNoMinimum intensity filter (0-1). Preferred name for new callers.
max_intensityNoMaximum intensity filter (0-1). Preferred name for new callers.
Behavior4/5

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

With no annotations provided, the description carries full burden. It explains the ranking algorithm, optional blending, and output sorting, which adds value beyond the schema. No contradictions.

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?

Single sentence, front-loads key action, includes optional features and output format. No wasted words.

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

Completeness4/5

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

Given 14 parameters and no output schema, the description covers the core purpose and output. It could mention result limits or filter interactions, but the schema fills in details.

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 description coverage is 100%, so baseline is 3. The description summarizes key features but does not add new meaning per parameter.

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 does semantic search using BM25 with optional features and returns sorted results. It distinguishes from siblings by specifying the search method and output format.

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

Usage Guidelines3/5

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

The description does not explicitly compare to sibling tools like retrieve_memory or list_memories, but the purpose is implied. No guidance on when to use or not use is given.

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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