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search_memories

Search agent memories with semantic matching. Use a query to find relevant memories via vector similarity or text fallback.

Instructions

Semantic search across agent memories. Returns memories matching the search query using vector similarity (when available) or text ILIKE fallback. Requires Professional tier or higher.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results (default 10, max 50).
queryYesSearch query to match against memory keys and values.
Behavior4/5

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

No annotations are provided, but the description discloses the search mechanism (vector similarity or ILIKE fallback) and implies read-only behavior, providing good 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?

Two sentences with no extraneous words; the first sentence captures the core purpose, making it efficient and front-loaded.

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?

The description explains functionality and parameters adequately but omits details about the return format, which would be helpful given no output schema.

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 description adds minimal value beyond the schema—only clarifying that the search is semantic and matches keys and values.

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 explicitly states the tool performs semantic search across agent memories, using vector similarity or ILIKE fallback, which clearly differentiates it from the unrelated financial siblings.

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

Usage Guidelines3/5

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

The description mentions the Professional tier requirement, giving a prerequisite but no explicit guidance on when to use versus alternatives, which are absent.

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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