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mem_recall

Retrieve relevant memories using hybrid search across vectors, keywords, and knowledge graph. Filter by project, session, or type to find architecture decisions, bug fixes, and patterns.

Instructions

Primary retrieval tool. Runs fused hybrid recall across sentence vectors, chunk vectors, keyword FTS, and knowledge-graph enrichment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hydeNoRequest HyDE query expansion when configured
modeNocompact returns evidence lines; context includes retrieved text
typeNoOptional observation type filter
debugNoInclude retrieval defaults and semantic input sources
limitNoMaximum evidence items (default: 5)
queryYesRecall/search query
scopeNoOptional scope filter
projectNoOptional project filter
time_toNoOptional inclusive created_at upper bound
time_fromNoOptional inclusive created_at lower bound
topic_keyNoOptional exact topic_key filter
session_idNoOptional session filter
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the hybrid recall method and mentions multiple vector and knowledge-graph sources, which is transparent. However, it lacks details on permissions, side effects, or return format.

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 zero waste. The first sentence immediately identifies the tool's purpose ('Primary retrieval tool'), and the second adds technical detail. Efficient and front-loaded.

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?

Despite 12 parameters and no output schema, the description does not explain the return format or how results are structured. This forces the agent to rely on inference or trial-and-error, reducing completeness.

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 schema already documents all parameters. The description adds no additional meaning beyond what is in the schema, such as explaining how parameters interact or providing default behaviors.

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 it is the 'Primary retrieval tool' and explains it runs 'fused hybrid recall across sentence vectors, chunk vectors, keyword FTS, and knowledge-graph enrichment,' which defines its function and distinguishes it from siblings like mem_get or mem_context.

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?

The description positions it as the primary retrieval tool, implying it should be used for general recalls. However, it does not explicitly state when not to use it or provide alternatives, leaving some ambiguity.

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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