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moorcheh-ai
by moorcheh-ai

recall

Search agent memories using natural language queries to retrieve semantically similar past information, avoiding the need to ask users for repeated details.

Instructions

Search the agent's memories by semantic similarity. Returns the top-N most relevant items. Use this FIRST before asking the user to repeat information - the agent may already remember it. The query should be natural language ('what does the user prefer for code style?'), not keywords.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional type filter - e.g. ['preference'] to only retrieve user preferences.
limitNoMax number of memories to return (1-100).
queryYesNatural-language search query.
agent_idNoMemanto agent identifier the memory belongs to (required: no MEMANTO_DEFAULT_AGENT_ID is configured).
min_similarityNoMinimum similarity score 0-1.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNosemantic | recent | as_of | changed_sincesemantic
countNo
queryNo
statusYes
messageNo
agent_idYes
memoriesNo
Behavior4/5

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

Explains the search mechanism (semantic similarity), return behavior (top-N), and query format (natural language). Does not mention read-only nature, but no annotations are provided; description is sufficient for understanding behavior.

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?

Three sentences, front-loaded with purpose, efficient and clear. Every sentence adds value, no redundancy.

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

Completeness5/5

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

Given five parameters, full schema coverage, and output schema present, the description covers all necessary details: core functionality, usage hint, and query format. No gaps for agent invocation.

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 coverage is 100%, so baseline is 3. Description adds value by explaining that query should be natural language (not keywords) and provides an example, enhancing understanding beyond schema descriptions.

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 tool searches memories by semantic similarity and returns top-N items. It distinguishes from siblings like recall_recent and recall_changed_since by emphasizing semantic search and providing a usage hint to check memory before asking user.

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 explicit guidance to use this tool first before asking the user for information. Does not explicitly name sibling alternatives but implies when to use (for semantic recall) and what not to use (keywords). Could be improved by directly contrasting with other recall tools.

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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