graph-mind

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brain_context

Read-onlyIdempotent

Retrieve relevant past conversations and memories from Graph-MIND before replying, so responses reflect earlier facts, preferences, decisions, or work.

Instructions

Answer from the user's past: one bounded text packet to read before replying.

    The default memory call: use it whenever a request may depend on earlier conversations,
    facts, preferences, decisions or work. It returns `context`, a size-capped text of the
    relevant memories and conversation turns, each with its date and source. For structured
    rows, ids or one thing's history use brain_recall instead.

    Set `recent=true` whenever the question is about what happened lately or what the user was
    just doing: "what was I working on?", "where did we leave off?", "what did I do
    yesterday?", "최근에", "아까", "어제", "지금 뭐 하고 있었지?". Results then come newest
    first instead of by word overlap; naming something ("what did Kongi do lately") keeps them
    about that. Set `since` (ISO date or time) to drop anything earlier, e.g. "this week".
    Set `about_assistant=true` when the user asks what YOU said earlier: "which restaurant did
    you recommend?", "what were the steps you gave me?", "네가 아까 뭐라고 했지?". Your own
    past answers are then searched by meaning too, not only by their words.
    Set `around` when the question is about WHEN WE TALKED: "what did we discuss last week?",
    "지난달에 얘기한 거" -> resolve it yourself to a date or "start..end" (e.g. "2026-09-23"
    or "2026-09-01..2026-09-30"). Conversations from then rank first; nothing is dropped.
    Do not set it for when something HAPPENED ("the event I went to two weeks ago"): people
    mention events days or weeks before or after, so that date misleads (measured: on dev2
    it pushed the answering turn out in 2 of 11 questions and helped 2).

Input Schema

TableJSON Schema

Name	Required	Default
`as_of`	No
`limit`	No
`query`	Yes
`since`	No
`around`	No
`policy`	No	always
`recent`	No
`scopes`	No
`max_chars`	No
`about_assistant`	No
`knowledge_cutoff`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 15, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the annotations (readOnly/idempotent/non-destructive/openWorld=false), it discloses ordering semantics (recent makes results newest-first instead of word-overlap ranked), the size cap on output, and that each result carries a date and source. It also discloses that around re-ranks without dropping anything, a non-obvious behavioral trait.

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?

Purpose is front-loaded in the first sentence and each paragraph maps to a parameter decision, so most sentences earn their place. It is nonetheless long and drifts into loosely ordered asides (Korean examples, an empirical measurement) that could be tightened.

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?

Output schema exists, so return values needn't be described, and usage guidance is thorough. However, for an 11-parameter tool with zero schema descriptions, six parameters (as_of, limit, policy, scopes, max_chars, knowledge_cutoff) are silently undocumented, leaving an agent guessing on budget and scope control.

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 0% across 11 parameters, so the description must carry the load. It gives rich semantics for recent, since, about_assistant and around (with format examples like '2026-09-01..2026-09-30'), but as_of, limit, policy, scopes, max_chars and knowledge_cutoff are left entirely unexplained in both schema and description.

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?

States a specific verb and resource: retrieve a bounded text packet of past memories to answer from, and names its scope as the default memory call. It explicitly contrasts itself with brain_recall for 'structured rows, ids or one thing's history', so an agent can disambiguate from the closest 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 says to use it 'whenever a request may depend on earlier conversations, facts, preferences, decisions or work', then gives condition-by-condition guidance for recent/since/about_assistant/around with quoted example utterances. It even names a when-NOT case for around ('do not set it for when something HAPPENED'), with measured evidence.

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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