Skip to main content
Glama

Add Memory

add_memory

Store text memories with semantic indexing to enable search and integration into chat responses via Memory Augmented Generation. Use for notes, meeting summaries, and insights.

Instructions

Store a text memory with semantic indexing. Memories are searchable and integrated into personal chat responses via Memory Augmented Generation (MAG). Use this to store notes, meeting summaries, insights, or any text knowledge.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesText content to store as a memory
unique_idNoNamespace (default: 'default')
tagsNoComma-separated tags for categorization
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses semantic indexing, searchability, and MAG integration. However, it lacks details on mutability, persistence, size limits, or how duplicates are handled.

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 concise sentences front-load the core action and provide use cases. No redundant or irrelevant information.

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?

Given the tool's simplicity and lack of output schema/annotations, the description covers basic purpose and usage but misses behavioral details like limits or behavior on duplicates. Adequate but not comprehensive.

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

Parameters3/5

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

Schema coverage is 100% with clear parameter descriptions. The description adds minimal value beyond schema, only restating the purpose. Baseline score of 3 is appropriate.

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 text memory with semantic indexing. It distinguishes from siblings like list_memories and search_memories by explaining the stored memories are searchable and integrated via MAG.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit use cases: 'store notes, meeting summaries, insights, or any text knowledge.' It does not explicitly exclude other uses or mention alternatives, but the context with sibling tools implies when to use.

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/kennyzheng-builds/videoseek-mcp'

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