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mgi-mind

Official

mind_should_search

Classifies a user query to decide whether to search long-term memory before answering, returning priority, reason, and libraries to search first.

Instructions

Decide whether to search memory BEFORE answering a user query. Returns priority (must-search / should-search / answer-directly), the reason, and which libraries to search first. Call this on a turn when unsure; it implements the search-before-answer trigger policy (named project, meta-cue like 'did I tell you', negation to verify, cross-session reference). Advisory: it cannot force a search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe user's query to classify

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the return shape (priority / reason / libraries), states the advisory limitation ('cannot force a search'), and explains the policy it implements. It does not cover failure modes or cost, but the key behavioral traits are present.

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-loaded with the core action and the return values, then the trigger conditions and the advisory caveat. It is dense and slightly run-on but every clause adds decision-relevant information; nothing is padding.

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?

For a one-parameter classifier with no output schema, the description supplies purpose, trigger conditions, return values, and its own limitation, which is close to complete. Minor gaps are the absence of an explicit follow-up tool to invoke on a must-search verdict.

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?

There is only one parameter (query) and schema description coverage is 100%, so the schema fully documents it. The description adds policy context around when to supply the query but no new syntax or format meaning beyond the schema, making the baseline 3 appropriate.

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 — it decides whether to search memory before answering — and clearly distinguishes itself from actual search siblings (mind_search, mind_recall) by being an advisory classifier rather than a retrieval tool. An agent can tell what it produces (priority, reason, libraries) without opening the schema.

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?

Explicitly says to call it 'on a turn when unsure' and enumerates the trigger policy cases (named project, meta-cue, negation to verify, cross-session reference), giving clear usage context. It does not explicitly name the alternative tool to run after a positive decision (e.g., mind_search), so routing to siblings is only implied.

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