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Meta-consciousness-AI

mcai-knowledge

Official
by Meta-consciousness-AI
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Python
Hybrid

回答学说问题

mcai_answer
Read-onlyIdempotent

Answers natural-language questions about Meta-Consciousness theory by returning authoritative definitions, source quotations, and related concepts from the local knowledge base.

Instructions

回答关于元意识学说的整句问题，返回权威定义、原文引证与相关概念。适合：用户用自然语言提问、且没有明确指定只要某一条词条时，例如“什么是元意识”“元识和元知是什么关系”“序生智能体如何解释 AI 涌现”。分工：若已知确切术语、想拿完整词条（别名、出处、引证、关系），用 mcai_concept；若不确定术语写法，先用 mcai_search；只看目录用 mcai_list；只看关系用 mcai_related。边界：只基于本知识库语料回答，未收录的概念会返回 type=none；不要据此推断、补写或引入库外语义。

Input Schema

NameRequiredDescriptionDefault
questionYes一个完整的自然语言问题，建议 5–120 字，一次只问一个问题；中英文均可。建议包含明确的术语，例如“什么是元意识”“元灵和元子有什么区别”。

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.2
    • changedInput schema / properties / question / description
      Previous value: -"要回答的问题，中英文均可，例如“什么是元意识”“元灵和元子有什么区别”"New value: +"一个完整的自然语言问题，建议 5–120 字，一次只问一个问题；中英文均可。建议包含明确的术语，例如“什么是元意识”“元灵和元子有什么区别”。"
    • addedInput schema / properties / question / examples
      Added value: +[
+  "什么是元意识",
+  "元灵和元子有什么区别"
+]
  2. Changed2 schema fields changedv0.1.1
    • addedInput schema / additionalProperties
      Added value: +false
    • changedInput schema / properties / question / description
      Previous value: -"用户问题，中英文均可"New value: +"要回答的问题，中英文均可，例如“什么是元意识”“元灵和元子有什么区别”"
  3. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations, which already declare it is a read-only, idempotent, closed-world operation, the description discloses the boundary: answers come only from the local corpus, unlisted concepts return type=none, and the agent must not extrapolate outside the library. The response shape (definitions, citations, related concepts) is also disclosed, which matters because no output schema exists.

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?

It is front-loaded: purpose and return types come first, then usage examples, then the routing table for siblings, then boundaries. The four-way sibling enumeration is somewhat heavy but each branch carries real routing value, so no sentence is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Because there is no output schema, the description must carry the return semantics. It does so (definitions, source quotes, related concepts, type=none for unlisted concepts), and it also clarifies the closed-world scope and the anti-hallucination rule, leaving no gap that an agent needs filled before calling this 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% and the question parameter already carries examples, length guidance (5–120 chars), and a one-question-per-call note. The description's '整句问题' (whole-sentence question) framing is consistent with the schema but adds little beyond it, so the baseline of 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 first sentence names the specific verb and resource: answer full-sentence questions about 元意识学说, returning authoritative definitions, source citations, and related concepts. It also distinguishes itself from each sibling by naming the exact conditions under which mcai_concept, mcai_search, mcai_list, and mcai_related are the better choice.

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 explicitly states when to use this tool (natural-language question, no single term specified) with three concrete example questions. It then routes to alternatives for the opposite case (known exact term, full entry, unsure spelling, catalog-only, relations-only), so nothing is left to 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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