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MyAITwin

by com.lutolearn
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search_for_creation

Read-only

Use this BEFORE any creation task ("help me write X", "I'm working on Y"). Runs two parallel searches and returns them separately: a SKILLS bucket (skill/voice/template, the craft layer) and a KNOWLEDGE bucket (knowledge/principle/brand/idea/resource, the material). Bring both into context before producing output. If the skills bucket is empty and output_type is set, this also increments a skill-gap counter; when count reaches 3 the response includes skill_gap.skill_gap_threshold_reached: true so you can prompt the user to codify a skill.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesWhat you're looking for. Natural language, the task or topic at hand.
output_typeNoWhat kind of output the user is creating (e.g. "linkedin-post", "client-proposal", "follow-up-email"). Used to track the skill gap if no matching skill exists.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / query / description
      Previous value: -"What you're looking for — natural language, the task or topic at hand"New value: +"What you're looking for. Natural language, the task or topic at hand."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond readOnlyHint annotation, the description reveals two parallel searches, separate buckets, and a skill-gap counter mechanism that modifies response when threshold reached. This is rich behavioral context not in annotations.

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?

Packs significant information into a few sentences; front-loads usage advice. Could be slightly tighter but remains efficient.

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

Completeness5/5

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

Fully explains what the tool returns (two buckets), when to use, how params affect behavior (skill gap), and the output signal. No output schema exists, so this description is complete for agent understanding.

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?

Both parameters have schema descriptions. The description adds context: query is 'natural language' task/topic, and output_type is used for skill gap tracking. Provides usage nuance beyond schema.

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?

Specifically states 'Use this BEFORE any creation task' and describes two parallel search buckets (SKILLS and KNOWLEDGE), clearly indicating it's a pre-creation search tool distinct from siblings.

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 tells when to use ('before any creation task') and what to do with results ('bring both into context before producing output'). Does not explicitly list alternatives or when not to use, but the context is clear.

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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