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allenc84
by allenc84

save_memory

Record decisions, insights, and context from conversations as structured memories with types and importance weights.

Instructions

Save a new memory. Use this to record decisions made, insights surfaced, how the user's thinking has evolved, feedback given, or important context from this conversation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoPrimary topic tag (e.g. 'product-launch', 'hiring', 'q3-planning')
sourceNoWhere this came fromconversation
contentYesThe memory content — be specific and self-contained
salienceNoImportance weight 0.0–1.0. High-stakes decisions = 0.9+. Routine context = 0.3–0.5.
namespaceNoNamespace to save into. Omit for this server's namespace.
memory_typeYesType of memory. One of: ['episodic', 'feedback', 'project', 'reference', 'semantic', 'user']
Behavior3/5

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 does not disclose any behavioral traits such as persistence, overwrite behavior, or side effects beyond stating it saves a memory.

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 concise—two sentences with no wasted words. The purpose is front-loaded and each sentence contributes meaningful context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description covers the save action well but does not mention return value or confirmation. However, for a straightforward save operation, it is sufficiently complete.

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

Parameters4/5

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

Schema description coverage is 100%. The description adds value by specifying that content should be 'specific and self-contained' and gives examples for salience values, going beyond schema descriptions.

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 explicitly states the tool saves a new memory and lists specific use cases (decisions, insights, feedback, etc.), clearly distinguishing it from sibling tools like search_memory and get_context_brief.

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 provides clear guidance on when to use the tool (recording important conversation context), but does not explicitly mention when not to use it or alternatives.

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

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