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Metis · Memory Curator — Store Semantic Memory

store_semantic_memory

Store a concept and its definition in semantic memory to surface relevant knowledge without relying on event history.

Instructions

Store a distilled knowledge node in semantic memory.

Stores a distilled CONCEPT/definition (timeless 'what I know'). For a
time-stamped event use store_episodic_memory; for a human-curated palace
note use add_memory_entry.

Semantic memory holds the 'what I know' layer — concepts, definitions,
and their relationships. Used by retrieval to surface relevant knowledge
without relying on raw event history.

Args:
    concept: Short name for the concept, e.g. 'RDT sensitivity' or
        'fAChE inhibition'.
    definition: A one-to-three-sentence definition or explanation.
    related_concepts: Comma-separated names of related concepts.
    source_type: Where this came from: 'paper', 'note', 'idea', or
        'user_defined'.
    source_id: ID of the source record, e.g. a paper DOI or idea_id.

Returns:
    A single TextContent confirming the stored node (its row id and concept
    name), or an error message if the database is missing, fastembed is not
    installed, or the write fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conceptYes
source_idNo
definitionYes
source_typeNouser_defined
related_conceptsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.7/5.0
Behavior4/5

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

Without annotations, the description covers the behavior well: it stores a concept, is part of semantic memory, and mentions error conditions (missing database, missing fastembed, write failure). However, it does not explicitly state whether the operation is idempotent or if it overwrites existing entries, and lacks details on side effects.

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 a clear intro, usage guidance, parameter list, and return info. It is slightly verbose but still efficient. Every sentence adds value, though some repetition could be trimmed (e.g., 'what I know' is stated twice).

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 5 parameters (2 required), no nested objects, and an output schema (mentioned), the description covers all essential aspects: purpose, usage context, parameters, and return values. It is complete for an AI agent to correctly select and invoke the tool.

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

Parameters5/5

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

Schema coverage is 0%, but the description provides detailed parameter explanations including examples for concept, definition, related_concepts, source_type (with enumerated values), and source_id. This fully compensates for the missing schema descriptions.

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 it stores a 'distilled knowledge node in semantic memory' for timeless concepts, and explicitly contrasts with store_episodic_memory and add_memory_entry, making the purpose unambiguous.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool (for storing concepts/definitions) and when not to (use store_episodic_memory for time-stamped events, add_memory_entry for curated notes). It also explains the role of semantic memory in retrieval.

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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