Skip to main content
Glama

memory_store

Store durable project memories in four classes—procedural, semantic, episodic, entity—so preferences, facts, events, and named items remain available across tasks.

Instructions

Store durable project memory.

Use procedural for preferences, conventions, and decisions; semantic for stable facts; episodic for events or task outcomes; and entity for named people, projects, or objects. Use a stable context for project memory.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes
contextNo
metadataNo
confidenceNo
provenanceNo
memory_classNosemantic
Behavior2/5

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

With no annotations, the description itself carries the burden of disclosing runtime behavior. It adds the useful detail that memory is 'durable,' but it does not say whether the call creates or updates an existing memory, whether it may overwrite previous data, what will be returned or persisted, or any notable side effects. That is a significant transparency gap.

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 brief, front-loaded with the primary purpose, and contains no filler or repetition. Every line contributes either to the core verb/resource definition or to the distinction among memory classes needed for correct invocation.

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?

With six parameters, no output schema, no annotations, and a large sibling toolset, the description leaves important context unstated. An agent cannot determine what the tool returns, how this differs from batch storage or recall, whether context is mandatory, or how memory behaves when stored again.

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?

This description is the only documentation for parameters since schema description coverage is 0%. It valuably explains the memory_class enum values and advises about the context parameter. Still, metadata, confidence, and provenance receive no meaningful explanation, so it only partially compensates for the missing schema descriptions.

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 opens with a specific verb and resource: 'Store durable project memory.' This clearly communicates the tool's core function. However, it does not differentiate itself from sibling tools like memory_store_batch or memory_recall, so it falls short of a 5.

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 provides solid guidance for the memory_class parameter, mapping procedural, semantic, episodic, and entity memory types to concrete usage patterns. However, it does not say when to choose this tool over related siblings such as memory_store_batch, memory_recall, or memory_suppress, and it offers no explicit exclusions.

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/imattau/polypack-mcp'

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