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LaminateAnalyzerMCP

LaminateAnalyzerMCP

by squall321
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

get_reference_cases

Retrieve reference laminate cases with complete input payloads and expected outputs for use as few-shot examples or server self-validation.

Instructions

내장 기준 케이스를 반환한다. case_id 생략 시 목록.

각 케이스는 완전한 입력 payload(input.laminate)와 폐형해 기대값(expected)을 포함하므로, 입력 스키마의 실전 예시(few-shot)이자 서버 자가 검증 수단으로 쓸 수 있다.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_reference_cases"
  }
}

Input Schema

NameRequiredDescriptionDefault
case_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It correctly implies a read-only operation ('returns'), but does not disclose other behavioral traits like idempotency, rate limits, or safety. The description adds some value by stating the content of each case (payload and expected value), but is not fully transparent.

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 two concise sentences. The first states the primary purpose and conditional behavior; the second explains additional value. Every sentence is purposeful, with no redundancy or verbosity.

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 the tool's simplicity (one optional parameter, no output schema), the description adequately covers behavior (list vs detail), content (payload and expected values), and use cases. It could mention output format or pagination, but not critical. A minor gap in completeness.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaning to the single parameter case_id by stating that omission returns a list, which the schema alone does not convey. This is a clear addition 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 'returns built-in reference cases' and distinguishes it from sibling tools (compute_neutral_axis, run_sensitivity_analysis, get_server_info) which perform different tasks. It specifies the optional case_id parameter for filtering, leaving no ambiguity about the tool's purpose.

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?

While it doesn't explicitly compare to siblings, the description indicates usage contexts: as few-shot examples and server self-validation. This gives implicit guidance on when to use the tool. However, it lacks explicit when-not-to-use or alternative recommendations.

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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