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Search Memories

search_memories

Search stored text memories using natural language queries. Returns relevant results ranked by semantic similarity.

Instructions

Semantic search across your stored text memories. Returns the most relevant memories ranked by similarity to your query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query
unique_idNoNamespace (default: 'default')
pageNoPage number (default: 1)
page_sizeNoResults per page (default: 20)
Behavior3/5

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

No annotations are provided, so the description carries full burden. It states the tool performs semantic search and ranks results by similarity, which is appropriate for a read-only operation. However, it does not disclose potential behavioral traits like rate limits or the response structure (e.g., no output schema). The lack of destructive behavior is implied but not explicit.

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 single sentence that efficiently conveys the tool's purpose and key behavior. Every word adds value, with no redundancy. It is front-loaded with the core action ('Semantic search').

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 tool's simplicity, no output schema, and sibling context, the description is adequate. It explains the search behavior and ranking, which is sufficient for an agent to decide when to invoke this tool. Minor improvement would be to mention the output format (e.g., list of memories with scores).

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 baseline is 3. The description adds no extra semantics beyond the schema; 'query' is labeled as 'Natural language search query' which matches the description, but no further details on query syntax or similarity semantics. Parameters like 'unique_id', 'page', 'page_size' are self-explanatory.

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 uses a specific verb ('search') and resource ('text memories'), clearly indicating the tool's function. It distinguishes from siblings like 'list_memories' (which lists all) and 'search_audio' (which searches audio). The addition of 'semantic search' and 'ranked by similarity' adds precision.

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 implies usage for semantic textual search but lacks explicit when-to-use or when-not-to-use guidance. It does not mention alternatives like exact match or when to use 'list_memories' vs this tool. The context is clear but exclusions 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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