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allenc84
by allenc84

save_memory

Record decisions, insights, and feedback from conversations as persistent memories with type, importance, and topic tags.

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.
memory_typeYesType of memory. One of: ['episodic', 'feedback', 'project', 'reference', 'semantic', 'user']
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states 'Save a new memory' without addressing side effects, persistence, idempotency, or limits. The lack of behavioral detail leaves uncertainty about what the tool does beyond creation.

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 two sentences: the first states purpose, the second lists use cases. It is front-loaded, concise, and every word adds value without redundancy.

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?

With five parameters, two required, no output schema, and no nested objects, the description adequately covers what the tool does and when to use it. It could mention that memories are stored persistently, but overall it provides sufficient context for an agent.

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%, so the baseline is 3. The description adds context for when to use certain memory types (e.g., 'feedback given'), but does not add meaning beyond the parameter descriptions already present in the schema.

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 saves a new memory and lists specific use cases like decisions, insights, and feedback. It distinguishes from siblings like list_memories, consolidate, and get_related by focusing exclusively on creating new entries.

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 concrete scenarios for use (e.g., recording decisions, feedback), but does not explicitly state when not to use the tool or mention alternatives. However, the diverse sibling tools imply boundaries, making the guidance clear enough.

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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