Skip to main content
Glama

memory_remember

Save user or agent facts into long-term memory by providing a key, value, and importance score. Facts persist across sessions.

Instructions

Save a fact to long-term memory (L4 CoreMemory).

Args: layer: "user" for user facts, "agent" for agent identity user_id: User identifier key: Fact key (e.g. "name", "language", "principle") value: Fact value importance: Importance score 0.0-1.0 (default 0.5) session_id: Session ID for dedup (optional)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNo
layerNouser
valueNo
user_idNodefault
importanceNo
session_idNo
Behavior2/5

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

No annotations exist, so the description must disclose behavioral traits. It says 'Save a fact' (write), but omits side effects like overwriting, permissions, idempotency, or return values. The importance parameter is not explained in terms of its effect on memory.

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 concise with a one-line summary followed by a structured parameter list. No redundant sentences. Could be slightly more compact, but overall efficient.

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

Completeness3/5

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

Input parameters are well explained, but the description lacks information about output or side effects. For a write tool with no output schema or annotations, this is adequate but not fully complete.

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

Parameters4/5

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

Schema coverage is 0%, so the description compensates by detailing each parameter in the Args section, including default values, allowed values for layer, and purpose of session_id for dedup. Adds significant meaning beyond the schema.

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?

The description clearly states 'Save a fact to long-term memory (L4 CoreMemory)', which is a specific verb and resource. However, it does not distinguish from siblings like memory_recall or memory_forget, lacking differentiation.

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?

No guidance is provided on when to use this tool versus alternatives. The description only lists parameters without context on scenarios, which is insufficient given many sibling tools.

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/Cipher208/mcp-ariel-memory'

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