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Glama

memory_add

Store or update a memory fact for an AI agent, with optional category and content to enable structured, recallable knowledge.

Instructions

Add or update a memory fact. Supports explicit category and optional long-form content payload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
valueYes
entityYes
contentNo
categoryNofact
propertyYes
confidenceNohigh
content_typeNotext/plain

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.9.1

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does reveal an upsert behavior ('add or update') and a default fact category, which is useful. But it fails to state what 'update' actually does — overwrite, merge, or version — or how confidence affects storage, which matters for a write tool with zero annotation coverage.

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?

Two tight sentences with the verb–resource pairing front-loaded in the first sentence. The second sentence earns its place by flagging the two parameters that differ from a plain fact write.

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

Completeness2/5

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

For an 8-parameter tool with 3 required fields and zero annotations, this is under-specified. The output schema covers return values, but the description still lacks conflict behavior, required-parameter semantics, and selection criteria versus memory_update and memory_correct. An agent would need to infer most operational details from parameter names alone.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for the schema's bare parameter titles. It adds meaning for category ('explicit') and content ('optional long-form payload') but says nothing about the three required parameters entity, property, value, nor about tags, confidence, or content_type. The core semantics of the required triple remain unexplained.

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?

States a clear action and resource: 'Add or update a memory fact.' It also flags two meaningful capabilities — explicit category and optional long-form content. However, it does not differentiate from the sibling memory_update, whose name suggests the same 'update a memory fact' operation.

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?

Provides no guidance on when to use this tool versus the 38 siblings. In particular, it does not distinguish itself from memory_update or memory_correct, leaving the agent to guess which write path is appropriate. Context signals show siblings exist, but the description itself offers no selection criteria.

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