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madgodinc

mgi-mind

Official

mind_recall

Recall error-to-fix playbooks for an error signature or task context, ranking by relevance and trust so verified fixes rank higher and failing fixes are demoted.

Instructions

Recall error->fix playbooks for an error and/or a task context. Ranking blends relevance with trust: a verified or proven fix gets a boost, a repeatedly-failing one is demoted, but a strongly-matching unverified fix still surfaces.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoError signature to match (lexical)
limitNoMax playbooks
contextNoTask description to match (semantic)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does disclose the non-obvious ranking logic: verified/proven fixes are boosted, repeatedly-failing ones demoted, and strongly-matching unverified ones still surface. That materially helps an agent interpret and trust ordering. It still omits read-only status and the shape of a returned 'playbook'.

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?

Two sentences, purpose front-loaded before the ranking detail, with no filler. The second sentence is slightly dense but every clause describes a distinct ranking behavior that earns its place.

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?

For a 3-parameter recall tool with no output schema and no annotations, the description explains purpose and ranking but never describes what a returned playbook contains or the response shape. Adequate but with a clear gap given structured data cannot fill it.

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 description coverage is 100%, so error (lexical), context (semantic), and limit (default 3) are already fully documented. The description adds the 'and/or' combinatorics — that either or both matching inputs may be supplied — but little else beyond what the schema provides, so the baseline 3 holds.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Recall error->fix playbooks') with additional scope ('for an error and/or a task context'). An agent can tell what the tool returns without opening the schema. It does not, however, explicitly distinguish itself from the sibling mind_recall_all, which is the natural alternative an agent would weigh.

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 phrase 'for an error and/or a task context' implies the situation in which to reach for it, but there is no explicit when-to-use, when-not-to-use, or named alternative (e.g. mind_recall_all, mind_search). Usage is inferable rather than stated.

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