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yamantaka520

agent-memory-os

memory_search

Find relevant memories by natural-language query, ranked by relevance. Results respect access controls: private, team, project, and global scopes.

Instructions

Recall memories relevant to a query, ranked best-first.

    Call this before answering to retrieve what you already know. Results are
    access-controlled: only memories this agent (AGENT_MEMORY_AGENT_ID) is
    allowed to see are returned — private, its own, its teams'/projects', and
    global. Each result has `id`, `score` (relevance), `content`, `scope`, and
    `type`. Returns an empty list if nothing relevant is visible.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return, best-first.
ownerNoOptional filter to a single owner id. Leave unset to search everything this agent may see.
queryYesNatural-language search query. Matches by keyword AND by association (linked memories surface even without shared words).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 discloses access control, result fields, and query matching behavior. However, it does not explicitly state that the tool is read-only or non-mutating, which would enhance transparency.

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 a short, front-loaded paragraph with no unnecessary words. Every sentence adds meaningful information.

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 the complexity and number of sibling tools (11), the description adequately explains the tool's behavior, result structure, and access control. Minor omission: does not mention error handling for invalid queries, but this is acceptable.

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%, so baseline is 3. The description adds value by explaining that the query uses natural language and matches by association, which is not in the schema's description. It also clarifies the owner filter optionality.

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 action ('Recall memories'), the resource ('memories'), and the ranking ('ranked best-first'). It distinguishes from sibling tools like memory_add and memory_link.

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 explicit when-to-use guidance ('Call this before answering to retrieve what you already know') and explains access control. It does not explicitly state when not to use or compare to alternatives, but the context from sibling tools makes it clear.

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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