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memo_save

Persist content as memory, optionally extracting atomic facts for detailed recall. Manage visibility via scope and tags.

Instructions

Persist content to memo.

When extract is true (defaults to the MEMO_SAVE_EXTRACT flag, off), the helper LLM decomposes content into atomic facts and saves each as its own memory (mem0 ADD-model) instead of one opaque blob; tags propagate to every fact. Returns an extraction summary (status, saved ids, saved_titles, counts) rather than a single record. If nothing extractable is found, the blob is saved verbatim.

scope controls the auto project:<repo> tag for THIS call only: "global" skips it (the memory lands untagged → the global recall tier, +0.10 boost everywhere); "project" or None keep the default auto-detection. An explicit project: tag in tags always wins either way.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
typeNonote
extraNo
scopeNo
titleNo
contentYes
extractNo
auto_deriveNo
respect_synapse_freezeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Adds substantial behavioral details beyond annotations: extraction decomposition, return format (summary vs single record), tag propagation, scope behavior with auto-detection, and fallback to verbatim save. Annotations only indicate non-read-only and non-destructive, so the description fills in critical traits.

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?

Well-structured with paragraphs for main action, extraction, and scope. Front-loaded with core purpose. Slightly verbose in scope explanation, but generally efficient and clear.

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

Completeness3/5

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

Covers main behaviors (extraction, scope) and return format, and references an output schema. However, it omits explanations for several parameters and does not discuss error cases or performance implications, leaving moderate gaps for a complex tool with 9 parameters.

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?

With 0% schema description coverage, the description adds meaning for parameters like content, extract, tags, and scope (4 of 9). Other parameters (type, title, extra, auto_derive, respect_synapse_freeze) receive no explanation beyond their names, leaving gaps.

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 primary action: 'Persist content to memo.' It also explains the extraction feature and scope control, effectively distinguishing it from sibling tools like memo_save_text, which likely lacks extraction.

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?

Provides clear context on when to use the extract flag and scope parameter, including default behaviors and environment variable influence. However, it does not explicitly specify when not to use this tool or compare with alternatives like memo_save_text.

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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