Skip to main content
Glama

storeRequirement

Use this tool to store project requirements, including title, content, importance level, and optional metadata, in a long-term memory system for persistent recall and future reference.

Instructions

Stores a project requirement in the long-term memory

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesContent of the requirement
importanceNoImportance level (low, medium, high)medium
metadataNoOptional metadata for the requirement
titleYesTitle of the requirement
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool stores data in 'long-term memory', implying persistence, but doesn't cover critical aspects like whether this requires specific permissions, how data is retrieved or updated, potential rate limits, or error handling. For a storage tool with zero annotation coverage, this is a significant 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized for its function, with no wasted content.

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?

Given the tool's complexity (storage operation with 4 parameters, no output schema, and no annotations), the description is incomplete. It doesn't explain what 'long-term memory' entails, how to retrieve stored requirements, or potential side effects, making it inadequate for an agent to use effectively without additional context.

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?

The input schema has 100% description coverage, so the schema already documents all parameters (title, content, importance, metadata). The description adds no additional meaning beyond what the schema provides, such as examples or context for parameter use. Baseline 3 is appropriate when the schema does the heavy lifting.

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 the action ('Stores') and the resource ('a project requirement in the long-term memory'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'storeAssistantMessage', 'storeUserMessage', 'storeDecision', or 'storeMilestone', which all store different types of data in memory, so it lacks sibling distinction.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for storing requirements, or how it differs from other storage tools like 'storeDecision' or 'storeMilestone', leaving usage ambiguous.

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

Related 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/aiurda/cursor10x-mcp'

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