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memory_store

Save preferences, facts, and decisions to persistent long-term memory for AI agents. Use for notable information that must be recalled across sessions.

Instructions

Save important information in long-term memory. Use for preferences, facts, decisions, and other notable information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesInformation to remember
scopeNoMemory scope (optional, defaults to default scope)
topicNoTopic label for grouping related memories (e.g. "remotion", "invoice"). Auto-inferred from similar memories if omitted.
categoryNoMemory category
importanceNoImportance score 0-1 (default: 0.7)
lesson_ruleNoFor category="lesson": the derived rule to follow (e.g. "check for syntax errors before assuming cache issues")
lesson_triggerNoFor category="lesson": what situation triggers this lesson (e.g. "when editing CSS layout")
lesson_principleNoFor category="lesson": the universal principle (e.g. "verify assumptions with evidence before acting")

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.33

TDQS

B3.2/5.0
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. It doesn't disclose whether the operation is permanent, how deduplication works, what happens when a similar memory exists, or any return behavior. Only mutation is implied by 'Save'.

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-loaded with the core purpose, with no wasted words. Very efficient.

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 8 parameters, no annotations, and no output schema, the description is too sparse. It doesn't cover important behavioral aspects (e.g., overwriting, deduplication, scope defaults, importance defaults) that an agent needs to invoke the tool correctly, especially given the rich schema.

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 description coverage is 100%, so the schema already documents all parameters in detail. The description adds a list of example use cases but no parameter semantics beyond what the schema provides. Baseline 3 is appropriate.

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?

States a specific verb+resource ('Save important information in long-term memory') and gives examples of what qualifies. It's clear what the tool does, though it doesn't differentiate from sibling tools like memory_update or memory_merge.

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 lists when to use it ('for preferences, facts, decisions') but doesn't explicitly say when to use alternatives like memory_update (for changing existing memories) or memory_merge. Implied usage only.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.