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recall

Read-only

Retrieve relevant AI agent memories using hybrid vector, full-text, and keyword search with optional deep search and channel filtering.

Instructions

Recall relevant memories using multi-strategy search (vector + FTS5 + keyword).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesAgent identifier
queryYesSearch query (empty returns recent memories)
limitNoMax memories to return
deepNoDeep recall — disable time and completion decay for exhaustive search
channelNoFilter memories by channel (e.g. 'chat', 'discord'). Default: '' (all channels).
Behavior3/5

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

Annotations declare readOnlyHint=true, establishing safety. Description adds valuable behavioral context by disclosing the hybrid search methodology (vector + FTS5 + keyword) that determines how results are ranked and retrieved. Does not describe output format, pagination, or performance characteristics (e.g., latency of deep search).

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?

Single sentence, front-loaded with action verb. Every element earns its place: 'relevant' signals semantic ranking, 'multi-strategy' explains capability, parenthetical technologies specify implementation. Zero waste.

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?

Adequate given rich schema (100% coverage) and readOnly annotation. The search methodology disclosure helps. However, lacking an output schema, the description should ideally describe what constitutes a 'memory' object or return structure to be complete.

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%, providing baseline 3. Description adds meaningful context beyond schema: 'multi-strategy search' explains the query processing logic (vector + FTS5 + keyword) that the schema's generic 'Search query' label does not convey. Also implies 'relevant' ranking for the limit parameter.

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?

Clear specific verb ('Recall') + resource ('memories') + mechanism ('multi-strategy search'). Distinguishes from siblings like 'store' or 'delete_memory', and the mention of 'search' and relevance distinguishes from 'list_memories'. However, it does not explicitly name siblings or contrast use cases.

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?

Contains implied usage guidance through 'relevant memories' and 'search', suggesting use for lookup/retrieval rather than bulk enumeration. However, lacks explicit when-to-use/when-not-to-use rules or named alternatives (e.g., when to use 'list_memories' vs 'recall').

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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