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MCPaeroedu

MCPaeroedu

Official
by aerovfx
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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aeroedu_kg_extract

Extract key concepts and prerequisite relationships from educational texts to expand a knowledge graph. Analyzes textbooks, lectures, or handouts for automatic KG node addition.

Instructions

Trích xuất các khái niệm (node) Knowledge Graph từ văn bản hoặc tài liệu giáo khoa. AI (Claude) phân tích nội dung → xác định khái niệm chính, mối quan hệ prerequisite, và đề xuất thêm vào KG. Dùng để mở rộng KG từ giáo trình, bài giảng, sách giáo khoa.

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": {
      "text": "<text>",
      "subject": "<subject>"
    },
    "name": "aeroedu_kg_extract"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesNội dung văn bản cần phân tích (tối đa 20,000 ký tự). Có thể là đoạn sách giáo khoa, bài giảng, handout.
subjectYesMôn học của tài liệu
gradeNoLớp của tài liệu
auto_addNoTự động thêm node mới vào KG (true) hay chỉ preview (false). Mặc định false — nên preview trước khi thêm.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations are all false, indicating no destructive or read-only behavior. The description adds context: it suggests adding to KG, and the auto_add parameter defaults to false with a recommendation to preview first. This provides useful behavioral insight 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 a concise three-sentence paragraph. Every sentence provides essential information: purpose, process, and usage context. No redundancy or unnecessary details.

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?

The description covers purpose and the AI process. It mentions prerequisite relationships and preview recommendation. However, it lacks details on output format and error handling. Still adequate for a complex extraction 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% with detailed parameter descriptions. The tool description adds purpose but does not significantly enhance individual parameter understanding. The baseline of 3 is appropriate.

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 it extracts concepts (nodes) from text/educational documents for the Knowledge Graph. It specifies the AI analysis identifies main concepts and prerequisite relationships, and distinguishes from sibling tools like search or path finding.

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 explicitly states the tool is used to expand the KG from textbooks, lectures, and textbooks. While it doesn't mention when not to use, the context is clear. No alternative tools are referenced, which would improve clarity.

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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