Skip to main content
Glama
SVerITG

Metis Public Health

by SVerITG
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

store_semantic_memory

Save concepts and their definitions into semantic memory for targeted retrieval. Include related concepts and source details to enrich knowledge graphs.

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
definitionYes
related_conceptsNo
source_typeNouser_defined
source_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the tool stores in semantic memory and that retrieval uses this knowledge, and lists error conditions (missing database, missing fastembed, write failure). However, it does not disclose behaviors like idempotency, updating existing concepts, or side effects, leaving some gaps.

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 well-structured: a brief initial sentence stating purpose, then sibling comparisons, a paragraph on what semantic memory is, followed by clear Args and Returns sections. It is front-loaded with essential information and every part earns its place without unnecessary verbosity.

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 the tool's simplicity (store operation), the description is fairly complete: it explains the role of semantic memory, parameter semantics, and return type (TextContent with confirmation/error). It lacks details on handling duplicate concepts, but the error conditions and overall context suffice for a typical use case.

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?

The description includes an 'Args' section explaining each parameter in detail: 'concept' with examples, 'definition' as one-to-three sentences, 'related_concepts' as comma-separated, 'source_type' with enumerated possibilities, and 'source_id' as ID. This adds significant meaning beyond the schema's mere title/type, covering all 5 parameters.

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 stores a distilled knowledge node in semantic memory, using specific verb 'store' and resource 'semantic memory'. It distinguishes from sibling tools 'store_episodic_memory' (for time-stamped events) and 'add_memory_entry' (for human-curated palace note), 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 tells when to use this tool (for timeless concepts/definitions) and when not to: 'For a time-stamped event use store_episodic_memory; for a human-curated palace note use add_memory_entry.' This provides clear guidance on tool selection among similar siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/SVerITG/Metis_PH'

If you have feedback or need assistance with the MCP directory API, please join our Discord server