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Meta-consciousness-AI

mcai-knowledge

Official
by Meta-consciousness-AI
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Python
Hybrid

获取关系子图

mcai_related
Read-onlyIdempotent

Retrieves a concept's relationship subgraph, including causal, structural, and hierarchical links, to explain how terms connect and clarify parent/child or broader/narrower concepts.

Instructions

获取一个概念的关系子图（含衍生关系），用于解释概念之间的因果、结构与层级。适合回答“A 和 B 有什么关系”“这个概念的上位/下位概念是什么”。分工：需要完整词条用 mcai_concept；需要整句解释用 mcai_answer；需要找词用 mcai_search。边界：只返回库内已登记的关系，没有关系时明确说明。

Input Schema

NameRequiredDescriptionDefault
termYes中心术语名，必须是库内已收录的术语，例如 元意识 / 序生智能体；不确定时先用 mcai_search。
depthNo关系展开深度：1 只取直接关系，2 取两跳；默认 1。

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.2
    • changedInput schema / properties / depth / description
      Previous value: -"关系展开深度：1 只取直接关系，2 取两跳；默认 1"New value: +"关系展开深度：1 只取直接关系，2 取两跳；默认 1。"
    • changedInput schema / properties / term / description
      Previous value: -"中心术语名，例如 元意识 / 序生智能体"New value: +"中心术语名，必须是库内已收录的术语，例如 元意识 / 序生智能体；不确定时先用 mcai_search。"
    • addedInput schema / properties / term / examples
      Added value: +[
+  "元意识",
+  "序生智能体"
+]
  2. Changed3 schema fields changedv0.1.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / depth / description
      Added value: +"关系展开深度：1 只取直接关系，2 取两跳；默认 1"
    • addedInput schema / properties / term / description
      Added value: +"中心术语名，例如 元意识 / 序生智能体"
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: only relations registered in the knowledge base are returned, and absence of relations is reported explicitly rather than silently. It does not describe result shape or size, keeping it below a 5.

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?

A single front-loaded paragraph leading with the core capability, then the question types, then the division of labor and boundary. Every clause carries information; it is dense but not padded, with only mild redundancy between the usage examples and the boundary note.

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?

For a two-parameter read-only lookup with no output schema, the description covers purpose, the kinds of relations returned, sibling routing, and what happens when no relations exist. A brief note on result shape would make it airtight, but nothing needed to call it correctly is missing.

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 coverage is 100%, so both `term` and `depth` (including the 1–2 range and default) are already fully documented in the schema. The description reinforces the constraint that the term must already exist and points to mcai_search as a fallback, but adds no new syntax or format detail. Baseline 3 applies.

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 states a specific verb and resource ("获取一个概念的关系子图（含衍生关系）") and immediately characterizes the relation types (causal, structural, hierarchical). It explicitly contrasts itself with mcai_concept, mcai_answer and mcai_search, so an agent can distinguish it from every sibling without opening a schema.

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

Usage Guidelines5/5

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

It names the exact question shapes it answers ("A 和 B 有什么关系", "上位/下位概念") and then gives explicit routing: full entries go to mcai_concept, sentence-level explanation to mcai_answer, term lookup to mcai_search. That is when-to-use plus when-to-use-something-else, not inference.

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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