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brain_recall

Read-onlyIdempotent

Inspect or list stored memories and turns as structured rows to check IDs, fields, or one entity's ordered history—not a ready context.

Instructions

Raw lookup: the matching memories and turns as structured rows, not a ready context.

    To answer the user's question from their past, use brain_context instead; it returns one
    bounded text packet ready to read. Use brain_recall to inspect or list: the ids and fields
    of what matched (e.g. before brain_forget or `supersedes_memory_id`), one thing's history
    in order, or what the store knows about.

    `entity` answers "what happened to X, in order": every memory about that one thing (the
    auth module, this project, my address), oldest first, with replaced entries marked and the
    current one picked out. Similarity search returns only the nearest one or two of four
    decisions made about one thing in four sessions; this returns all four. With neither
    `query` nor `entity`, it lists what the store knows about, busiest first.

    `recent`, `since`, `about_assistant` and `around` mean the same as in brain_context.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNo
limitNo
queryNo
sinceNo
aroundNo
entityNo
recentNo
scopesNo
about_assistantNo
knowledge_cutoffNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), and the description adds genuinely new behavioral context: result ordering, how replaced entries are marked, how the current one is identified, similarity-vs-history recall behavior, and default list order ('busiest first'). This is exactly the value-beyond-annotations the dimension rewards.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the core distinction, then uses focused paragraphs per parameter cluster. Slightly long, and the cross-reference to brain_context for shared params offloads meaning elsewhere, but every sentence carries signal.

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?

An output schema exists, so return-format explanation is not required, and the description still characterizes the shape ('structured rows, not a ready context'). Given 10 params at 0% coverage, a few params (as_of, scopes, knowledge_cutoff) remain unexplained, but the recall-behavior model is otherwise well conveyed.

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 description coverage is 0% across 10 params, so the description must carry the burden and largely does: it explains entity semantics (every memory about one thing, oldest first, replaced entries marked), the no-query/no-entity default behavior, and defers recent/since/about_assistant/around to brain_context. It does not cover as_of, limit, scopes, or knowledge_cutoff, leaving several params undocumented anywhere.

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?

Opens with a precise definition of what the tool returns ('raw lookup: matching memories and turns as structured rows, not a ready context') and explicitly contrasts it with the sibling brain_context. An agent can distinguish it from every named sibling without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use brain_context (answering questions from the past) versus brain_recall (inspecting/listing ids and fields, one thing's history, store overview), and names concrete downstream uses like before brain_forget or supersedes_memory_id. No inference required.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.