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agentbay_memory_recall

Read-only

Retrieve relevant memories from your local AgentBay brain using lexical search. Filter results by tags, tier, type, and limit to find past coding insights and context.

Instructions

Recall memories from your local AgentBay brain via lexical search. Sign up to enable vector search and team-shared recall.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
tierNo
typeNo
limitNo
queryYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.2.5

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true and destructiveHint=false, so the description adds useful context without contradicting them: it is a lexical-search read of local memory, and advanced vector/team-shared behavior requires signup. This goes beyond the annotations by clarifying data locality and retrieval mode, though it does not fully describe all behavioral edge cases.

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 short sentences with no filler. The core purpose and method are front-loaded, and the second sentence adds a relevant capability boundary in minimal space. Every word earns its place.

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

Completeness2/5

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

For a tool with five parameters, no output schema, and zero schema description coverage, this description is too thin. It omits filter semantics, return behavior, pagination/limit implications, and any guidance on how the tier/type enums affect recall, leaving significant gaps for an agent attempting correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description bears the full burden of explaining parameters, but it mentions none of them. It never explains query, tags, tier, type, or limit, leaving the agent without any added meaning beyond the raw JSON schema names and enums.

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 action ('Recall'), the resource ('memories from your local AgentBay brain'), and the method ('via lexical search'). It differentiates this lexical/local recall from the vector and team-shared capabilities mentioned in the second sentence, making its scope distinct among 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 implies when to use the tool—for local lexical recall—and hints at an upgrade path for vector/team-shared recall by signing up. However, it gives no explicit guidance on when to choose this over sibling tools like agentbay_memory_verify, agentbay_memory_store, or agentbay_memory_health, and does not state exclusions.

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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