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Remember

Save key facts, lessons, and conversation fragments as structured memories with type, importance, and tags for reliable recall across sessions.

Instructions

Store something in memory.

Args: text: The content to remember. memory_type: One of 'episodic' (conversation fragment), 'semantic' (structured fact), or 'lesson' (correction/rule). key: Required for semantic type. Format: 'pref.theme', 'project.name', 'user.role', etc. importance: 0.0-1.0, how important this memory is. Higher = slower decay. Default 0.5. tags: Comma-separated tags for episodic memories.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNo
tagsNo
textYes
importanceNo
memory_typeNoepisodic

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden, and it adds useful detail: importance affects decay speed, key is required for semantic memories, and memory_type values map to concrete meanings. It does not discuss overwrite behavior or side effects, but the core persistence behavior is clearly disclosed.

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 compact, front-loaded with the one-line purpose, and uses an Args block where every line adds useful semantic information. No filler or repetition of schema defaults beyond what is helpful.

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?

The five parameters are all explained, and the output schema relieves the description from explaining return values. The only notable gap is the lack of explicit guidance about when Remember should be chosen over the sibling Learn 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 description coverage is 0%, so the description must fully document parameters, and it does: text, memory_type values, key format ('pref.theme' etc.), importance range/default, and tag format. It adds meaningful context beyond the raw schema.

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 first line, 'Store something in memory,' names a specific verb ('store') and a clear resource ('memory'), and the memory_type options further scope the action. It distinguishes this tool from sibling operations such as Recall, Forget, and PruneMemory, which imply retrieval or deletion.

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 implies when to use the tool by saying 'Store something in memory' and by explaining memory_type alternatives, but it never explicitly contrasts this with siblings like Learn or states when not to use it. An agent must infer that Recall is for retrieval and Learn may be for lessons.

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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