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twexam-mcp

twexam-mcp

by pass-and-prosper
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_model_answer

Fetch an AI-written sample answer for a Taiwan national exam essay question by question ID, with a disclaimer included.

Instructions

取申論題 AI 擬答（含免責聲明）。

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": {
      "qid": "<qid>"
    },
    "name": "get_model_answer"
  }
}

Input Schema

NameRequiredDescriptionDefault
qidYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

B3/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It does disclose one useful behavioral trait: the returned AI answer includes a disclaimer. However, it does not explain the response format, error behavior, or whether the answer is non-authoritative beyond the vague '免責聲明' note.

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?

The description is a single short sentence with no filler and front-loads the key object and action. It earns its place, though the overall under-specification keeps it from a perfect score.

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?

This is a simple one-parameter retrieval tool with no output schema, so the description need not be long, but it should at least clarify the return value and the role of qid. It states the return is an AI model answer with a disclaimer, which is a minimal but adequate core for the simplest calls.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one required parameter 'qid' with 0% description coverage, and the tool description never mentions it. The word '申論題' weakly implies qid is an essay-question ID, but no format, source, or usage detail is provided. The description does not compensate for the schema gap.

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?

The description uses a specific verb ('取' / retrieve) and a specific resource ('申論題 AI 擬答' / essay-question AI model answer), and it adds the detail that a disclaimer is included. It is clear enough to distinguish from 'get_answer_key' and 'get_grading_rubric' in general meaning, though it does not explicitly name those alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus siblings like get_answer_key, get_question, or get_grading_rubric. There are no prerequisites, exclusions, or alternative conditions. The only implicit signal is that an AI model answer is desired.

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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