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skill_transfer_test

Read-onlyIdempotent

Test skill transfer by applying a method from a trained case to a new, different scenario. Identifies whether knowledge or imitation is used.

Instructions

Avicenna transfer check: same universal, new particular. Returns a worksheet (host agent fills it). If only the trained case works, that is imitation — not knowledge. Read-only.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "book_id": "<book_id>",
      "trained_case": "<trained_case>",
      "fresh_case": "<fresh_case>"
    },
    "name": "skill_transfer_test"
  }
}

Input Schema

Name	Required	Description
`book_id`	Yes	Skill id.
`concept_id`	No	Optional curriculum concept id.
`fresh_case`	Yes	A genuinely new particular (different project/person/constraint).
`trained_case`	Yes	Case you already practiced with the method.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.2.0Jul 16, 2026

TDQS

A4.4/5.0

Behavior5/5

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

The description states 'Read-only' consistent with annotations (readOnlyHint=true, idempotentHint=true). It explains the return of a worksheet and the pedagogical intent, adding valuable context beyond annotations.

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

Conciseness5/5

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

The description is extremely concise: two sentences and a fragment. It front-loads purpose, return, and a key note, with no wasted words.

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?

Given annotations and output schema, the description covers purpose, return, and interpretation. It lacks explicit prerequisites or when to use vs siblings, but is largely complete for a well-annotated tool.

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 baseline is 3. The description frames trained_case and fresh_case in context but does not add new details about format or constraints beyond the 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?

The description clearly states the tool is for an 'Avicenna transfer check' testing knowledge transfer by comparing performance on a trained case vs a fresh case. It specifies the return type (worksheet) and distinguishes from imitation, making the purpose specific and 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?

The description implies usage for testing transfer and hints at interpretation ('If only the trained case works, that is imitation'). However, it does not explicitly state when to use this tool versus siblings like skill_grade or skill_match, though the concept is clear.

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