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get_skill_requirements

Retrieves the required information checklist for a specified student competition track, including mandatory fields, field specifications, and scoring pitfalls, so agents can ask users for essential details before drafting applications.

Instructions

获取指定赛道的信息采集清单(必填项、字段规范、评分避坑要点)。
Agent 在为用户撰写申报书前,应调用此工具获取必须向用户询问的关键信息清单。

参数:
  skill_name: 赛道名称(如 'innovation-research', 'national-scholarship')

Input Schema

NameRequiredDescriptionDefault
skill_nameYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the shape of the response (required items, field specs, scoring pitfalls) and its position in the workflow, but says nothing about whether it is read-only, whether results are cached, or what happens with an invalid or unknown skill_name.

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 sentences plus a compact parameter note, with the purpose and the pre-write trigger front-loaded. There is no filler, though the parameter block slightly duplicates the schema.

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?

With an output schema present, return-value details need not be spelled out, and the description covers purpose, timing, and parameter intent adequately for a single-parameter read tool. The main remaining gap is the absence of a valid skill_name list or error behavior.

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 explains skill_name as the track name and supplies two concrete example values ('innovation-research', 'national-scholarship'). It falls short of enumerating all valid values, leaving the agent to guess a valid identifier.

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 names a specific verb+resource (获取指定赛道的信息采集清单) and enumerates what the checklist contains (必填项、字段规范、评分避坑要点). It is clearly distinct from siblings like get_skill_info or generate_student_doc, though it does not explicitly contrast itself with them.

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?

It gives an explicit trigger: call this before writing the user's application document to obtain the list of key information that must be asked of the user. That is clear usage context, but it does not name alternatives or state when not to use it (e.g. versus get_skill_info).

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