Skip to main content
Glama

remember

Save facts, decisions, or preferences to persistent memory that retains across agent sessions. Encrypts content and generates vector embeddings for semantic search.

Instructions

Store a fact, decision, or preference in persistent memory so it survives across sessions. This is a write operation that creates a new memory record, encrypts the content at rest, and generates a vector embedding for semantic search. Use remember (not recall) when you learn something worth keeping: architecture decisions, user preferences, bug root causes, project conventions, or task outcomes. Do not use for ephemeral scratch data, secrets, or large files. Returns the memory ID and timestamp. Costs 1 operation against the API key's monthly quota (500 free, then paid). Use forget to delete outdated memories before storing corrections, to prevent contradictions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe information to store. Write as a complete, self-contained statement (not fragments). Include context: 'User prefers TypeScript for backend services' not just 'TypeScript'. Max 10,000 characters.
agent_idNoUnique identifier for this agent instance. Use a consistent value across sessions so memories are retrievable. Default: 'default'.default
user_idNoUser identifier, required when scope is 'user'. Links the memory to a specific user across all their agents.
tagsNoCategorical labels for filtering during recall. Use lowercase, consistent terms: 'preference', 'decision', 'architecture', 'bug-fix'. Max 20 tags, each max 100 chars.
scopeNoVisibility: 'agent' (only this agent sees it, default), 'user' (all agents for this user, requires user_id), 'org' (all agents in the organization, requires org membership).agent
Behavior5/5

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

Details write operation, encryption at rest, vector embedding generation, return values (ID and timestamp), and API quota cost. No annotations present, so description fully covers behavioral traits.

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?

Six succinct sentences, each adding distinct value. Front-loaded with purpose, followed by usage, behavior, parameters, and cost. No unnecessary words.

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?

For a write operation with 5 parameters and no output schema, description covers return value, quota, security features, and optimal usage patterns. No gaps identified.

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?

With 100% schema coverage, baseline is 3, but description adds valuable guidance: content format (self-contained with context), tag conventions (lowercase, consistent terms), scope visibility, and agent_id consistency for session persistence.

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?

Clearly states it stores facts/decisions/preferences in persistent memory as a write operation. Distinguishes from recall and forget by name, making 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?

Explicitly advises when to use (learned lasting info) vs not (ephemeral, secrets, large files). Names sibling tools recall and forget as alternatives for reading and deleting.

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/AlekseiMarchenko/central-intelligence'

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