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save_memory

Store facts, decisions, and preferences in long-term memory with local embedding and semantic indexing for retrieval across sessions.

Instructions

Save information to long-term memory. The content will be embedded locally and indexed for semantic search. Use this to store facts, decisions, preferences, or any context worth remembering across sessions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoOptional tags for categorization (e.g. ["personal", "preference"])
contentYesThe text content to store in memory
metadataNoOptional key-value metadata to attach to this memory (e.g. { "topic": "auth", "project": "api" })
importanceNoImportance level 1-10 (default: 5). Higher = more resistant to decay
memory_typeNoMemory category (default: general). Affects decay rategeneral
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that content is 'embedded locally and indexed for semantic search,' which is a behavioral trait beyond a simple save. It also implies persistence across sessions. No destructive behavior is indicated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences: purpose, technical behavior, and usage guidance. It is front-loaded and efficient, with no redundant information. It earns a high score for conciseness.

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?

For a tool with 5 params (one required), nested objects, and no output schema, the description provides sufficient context: it explains the core function, indexing behavior, and appropriate use cases. It does not detail parameter interactions, but the schema covers that.

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 baseline is 3. The description does not elaborate on individual parameters beyond what the schema already provides. It mentions 'facts, decisions, preferences' which loosely map to 'memory_type' but adds no detailed semantics.

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 'Save information to long-term memory' and specifies the types of content to store (facts, decisions, preferences). It distinguishes from sibling tools like search_memory and delete_memory by framing it as the primary creation action.

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 explicitly says when to use: 'store facts, decisions, preferences, or any context worth remembering across sessions.' It does not explicitly mention when not to use, but the context implies temporary data is inappropriate. The sibling tools provide further differentiation.

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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