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memory_search

Search and retrieve AI memories with temporal relevance scoring and observation filtering to find past information efficiently.

Instructions

Search memories with temporal scoring + observation filtering.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'temporal scoring + observation filtering' which provides some context about how the search works, but doesn't address critical behavioral aspects like whether this is a read-only operation, what permissions are needed, how results are ranked, or what happens with large result sets. The description adds minimal behavioral context beyond the basic search concept.

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 extremely concise at just 7 words, front-loaded with the core purpose ('Search memories') followed by key features. Every word serves a purpose with zero waste or redundancy, making it efficient and easy to parse.

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?

Given that there's an output schema (which handles return values), no annotations, and only 2 parameters (though with 0% schema coverage), the description provides the basic purpose but lacks sufficient detail about behavioral characteristics and parameter usage. For a search tool with temporal and filtering features, more context about how these work would be helpful, but the existence of an output schema reduces the burden somewhat.

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

Parameters2/5

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

The schema description coverage is 0%, meaning neither parameter has any description in the schema. The tool description mentions 'temporal scoring + observation filtering' which hints at how the 'query' parameter might be processed, but provides no specific guidance on query syntax, what 'temporal scoring' means, what 'observation filtering' entails, or how the 'limit' parameter interacts with these features. The description adds marginal semantic value but doesn't adequately compensate for the complete lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Search memories' with specific features 'temporal scoring + observation filtering', which is a verb+resource combination. However, it doesn't differentiate from sibling tools like 'entity_query' or 'memory_verify', which might also involve searching or querying operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. There are multiple sibling tools (entity_query, memory_verify) that might overlap in functionality, but the description offers no explicit when/when-not instructions or context for choosing this specific search tool.

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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