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awesome-student-ai-skills

awesome-student-ai-skills

by cuic19053-hue
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_skill_info

Retrieve detailed metadata and configuration for a specific student competition, research grant, or scholarship proposal track to understand requirements and settings before applying.

Instructions

获取指定申报材料赛道的详细元数据与配置。

参数:
  skill_name: 赛道名称或标识（如 'innovation-research', 'national-scholarship', 'challenge-cup'）

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": {
      "skill_name": "<skill_name>"
    },
    "name": "get_skill_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
skill_nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description bears the full burden. '获取' unambiguously marks this as a read operation, and the description names what is returned (元数据与配置), which is the key behavioral fact. It does not say what happens if skill_name is unknown (empty result vs error), nor does it mention any side effects or permissions.

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 short lines with the purpose front-loaded and the parameter note second; nothing is padded. The '参数:' block is slightly redundant with the schema but the examples earn their 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 single-parameter lookup with an output schema, return values need no explanation, and the description covers the input well. What is missing is routing context against the large sibling set, particularly get_skill_requirements, which an agent could easily confuse with this tool.

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 coverage is 0%, so the description must compensate — and it does, giving the parameter's meaning plus three concrete example identifiers ('innovation-research', 'national-scholarship', 'challenge-cup') that reveal the expected naming style. It stops short of stating whether the name or the alias form is accepted, or case sensitivity.

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+resource: 获取 (get) 指定申报材料赛道的详细元数据与配置 (detailed metadata and configuration of a given application track). An agent knows exactly what is returned conceptually. However, it does not distinguish itself from the sibling get_skill_requirements, which sounds like a closely overlapping lookup.

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?

Usage is implied by the name and description — it is the metadata lookup for a named skill — but the description never says when to call this instead of get_skill_requirements, list_student_skills, or route_student_skill. No prerequisites or exclusions are given.

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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