MCPサーバーとして提供される汎用プロンプト作成エンジンです。12の哲学的次元にわたって意図を測定し、クライアントがプロンプトを構築するための構築基盤を返します。
このエンジンはプロンプト自体を生成するわけではありません。プロンプトの構築をヒューリスティックなものではなく原則に基づいたものにするための、次元的な基盤（アクティブな構成要素、スペクトラムの対極、緊張関係、ジェム、スポーク、調和ペア、構築のための質問）を提供します。
# Install
pip install advanced-prompting-engine
# Or run directly via uvx
uvx advanced-prompting-engine.mcp.json に以下を追加します：
{
"mcpServers": {
"advanced-prompting-engine": {
"command": "uvx",
"args": ["advanced-prompting-engine"]
}
}
}Related MCP server: MCP Prompt Optimizer
このエンジンは、あなたの意図を12次元の哲学的多様体の中に配置します：
面 | サブ次元 | フェーズ |
存在論 | 特殊 ↔ 普遍、静的 ↔ 動的 | 理解 |
認識論 | 経験的 ↔ 合理的、確実 ↔ 仮定的 | 理解 |
価値論 | 絶対的 ↔ 相対的、量的 ↔ 質的 | 理解 |
目的論 | 直接的 ↔ 究極的、意図的 ↔ 創発的 | 理解 |
現象学 | 客観的 ↔ 主観的、表層 ↔ 深層 | 理解 |
倫理学 | 義務論的 ↔ 結果論的、行為者 ↔ 行為 | 評価 |
美学 | 自律的 ↔ 文脈的、感覚的 ↔ 概念的 | 評価 |
行為論 | 個人的 ↔ 協調的、反応的 ↔ 先行的 | 応用 |
方法論 | 分析的 ↔ 総合的、演繹的 ↔ 帰納的 | 応用 |
記号論 | 明示的 ↔ 暗示的、統語的 ↔ 意味的 | 応用 |
解釈学 | 字義的 ↔ 比喩的、著者意図 ↔ 読者反応 | 応用 |
ヒューリスティクス | 体系的 ↔ 直感的、保守的 ↔ 探索的 | 応用 |
各面は144の認識的観測点からなる12x12のグリッドです。位置によって分類（角/中点/辺/中心）、効力、スペクトラムへの所属が決定されます。12の面は6つの補完的なペア（立方体モデル）として構成され、共有面を通じて調和します。エンジンは、位置の対応関係を通じて緊張関係を、立方体階層の変調を伴うジェム（面間の統合）、スポーク（面ごとの行動シグネチャ）、および中心的なジェムの一貫性スコアを計算します。
ツール | 目的 |
| プライマリ — 意図または座標を入力し、構築基盤を出力 |
| 解釈 — 構築基盤を平易な言葉で読み解く |
| エキスパート — グラフ探索、ストレス試験、三角測量 |
| オーサリング — 矛盾検出を伴う構成要素と関係の追加 |
create_prompt_basis(intent="Design an ethical framework for autonomous vehicle decision-making")エンジンはこの意図を12の哲学的次元すべてにわたって特定し、以下を返します：
{
"coordinate": {
"epistemology": {"x": 4, "y": 4, "weight": 0.76},
"ontology": {"x": 6, "y": 5, "weight": 0.73},
"praxeology": {"x": 7, "y": 4, "weight": 0.72},
"heuristics": {"x": 5, "y": 3, "weight": 0.66},
"phenomenology": {"x": 7, "y": 4, "weight": 0.61},
"ethics": {"x": 6, "y": 4, "weight": 0.53},
"...": "...all 12 faces with (x,y) position and relevance weight"
},
"harmonization": [
{"pair": ["ontology", "praxeology"], "resonance": 0.15},
{"pair": ["axiology", "ethics"], "resonance": 0.05},
"...6 complementary pairs with resonance scores"
],
"spokes": {
"ontology": {"classification": "weakly_integrated", "strength": 0.042},
"epistemology": {"classification": "weakly_integrated", "strength": 0.039},
"...": "...per-face behavioral signatures"
},
"central_gem": {"coherence": 0.69, "classification": "highly_coherent"},
"construction_questions": {
"ethics": {
"template": "What moral obligations does this prompt impose or assume?",
"position_summary": "balanced Deontological/Consequential + moderately Agent-focused",
"meaning_mechanism": "composition",
"phase": "evaluation"
},
"...": "...12 position-specific philosophical questions to guide prompt construction"
}
}出力は以下を示します：この意図は主に知識の検証（認識論 0.76）、どのような実体が存在するか（存在論 0.73）、そして行動の構造（行為論 0.72）に関するものです。倫理学は0.53で記録されており、存在はしていますが支配的ではありません。調和は、存在論と行為論が強く共鳴していること（0.15）を示しており、理論的な「何が存在するか」が実践的な「どのように行動するか」と一致しています。
正確な制御のために、座標を直接渡すこともできます：
coordinate = {
"ontology": {"x": 0, "y": 0, "weight": 1.0}, # corner: particular + static
"ethics": {"x": 0, "y": 11, "weight": 0.9}, # corner: deontological + act
"methodology": {"x": 0, "y": 0, "weight": 0.8}, # corner: analytic + deductive
# ...all 12 faces with x (0-11), y (0-11), weight (0-1)
}
result = create_prompt_basis(coordinate=coordinate)スタック: Python + NetworkX (トポロジー) + numpy (計算) + SQLite (永続化) + MCP SDK
グラフ: 1873ノード、2279エッジ (12面 × 144構成要素 + 132ネクサス + 1中心ジェム)
パイプライン: 8ステージ (意図解析 → 座標解決 → 位置計算 → 構成要素解決 → 緊張分析 → ネクサス/ジェム分析 → スポーク分析 → 構築ブリッジ)
ジオメトリ: 潜在的な面間トポロジーとしてのベクトル平衡体（立方八面体）、6つの補完ペアのための立方体モデル
デプロイメント: 単一プロセス、stdioトランスポート、デーモンなし、外部依存関係なし
docs/DESIGN.md — 完全な設計仕様
docs/CONSTRUCT-v2.md — 構成要素の仕様（面、点、スペクトラム、ネクサス、ジェム、スポークとは何か）
docs/CONSTRUCT-v2-questions.md — ゾーン別の144の構築質問テンプレート
docs/adr/ — 13のアーキテクチャ決定記録
pip install -e ".[dev]"
pytest tests/ -v同梱のパッケージには、事前に計算されたBGE由来のアーティファクト（semantic_bridge.npz, semantic_vocab.json）が含まれています。これらをゼロから再構築する場合（例：極の同義語編集後）、ビルド用の追加パッケージをインストールしてください：
pip install -e ".[build]"
python -m nltk.downloader wordnet omw-1.4
python scripts/build_semantic_bridge.pyビルドにはBAAI/bge-large-en-v1.5（約1.3GB、HuggingFaceキャッシュに一度ダウンロード）と、頻度順序付けのためのwordfreqを使用します。実行時の依存関係には影響しません。エンドユーザーは事前に計算されたアーティファクトのみを受け取ります。
開発環境のセットアップとガイドラインについては CONTRIBUTING.md を参照してください。
脆弱性の報告手順については SECURITY.md を参照してください。
MIT
Measure intent across 12 philosophical dimensions and return a construction basis.
Use this before constructing any prompt where dimensional precision, philosophical coherence, or systematic completeness matters.
Provide either 'intent' (natural language) or 'coordinate' (JSON object with 12 faces, each having x, y, weight).
Output modes (mutually exclusive, focused takes priority):
Default: full output (~50KB) with all pipeline data
compact=true: summary fields only (~2KB)
focused=true: guidance-centric output (~500 bytes) with dominant dimensions, gaps, resonance, and coherence — what a prompt engineer needs
| Name | Required | Description | Default |
|---|---|---|---|
| intent | No | ||
| coordinate | No | ||
| compact | No | ||
| focused | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It outlines output modes (default, compact, focused) with approximate sizes, and describes the coordinate structure. It does not mention side effects, permissions, or rate limits, but as a read-like creation tool, the disclosure is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short paragraphs (about 100 words) that are front-loaded with the main purpose, followed by usage and output details. Every sentence adds value, no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 0 required params, 4 optional params, and an output schema, the description covers input options and output modes well. It could briefly name the 12 dimensions, but the output schema likely details that. Overall, sufficient for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully compensates. It explains that 'intent' is natural language, 'coordinate' is a JSON object with 12 faces (x, y, weight), and that compact/focused control output verbosity. This adds crucial meaning absent from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool measures intent across 12 philosophical dimensions and returns a construction basis. It uses specific verbs ('measure', 'return') and a clear resource, and distinguishes itself from siblings by focusing on basis creation for dimensional precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using this tool before constructing prompts where dimensional precision, coherence, or completeness matters. It also explains input options (intent or coordinate) and output modes, but does not directly compare to sibling tools or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Explore the philosophical manifold. Operations: list_faces, list_constructs, get_construct, get_neighborhood, find_path, get_spoke, stress_test, triangulate.
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes | ||
| face | No | ||
| x | No | ||
| y | No | ||
| target_face | No | ||
| target_x | No | ||
| target_y | No | ||
| coordinate | No | ||
| coordinate_a | No | ||
| coordinate_b | No | ||
| classification | No | ||
| provenance | No | merged |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Does not disclose side effects, permissions, rate limits, or any behavioral traits. Only states 'explore', which is non-committal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Short two sentences, but front-loading with vague phrase reduces impact. List of operations is useful but could be structured better. Minimal but not overly concise for the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 12 parameters and 0% schema coverage, description is severely incomplete. No explanation of operations, their required parameters, or return values. Output schema exists but unmentioned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must add meaning. It only lists operation names without explaining any parameter. Parameters like x, y, face, etc. are not described, leaving agent guessing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The verb 'explore' is vague and 'philosophical manifold' is abstract, but listing operations gives some idea. Not a tautology, but doesn't clearly state concrete purpose. Distinguishes from sibling tools implicitly via domain, but lacks explicit differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. Does not suggest scenarios or mention sibling tools. The list of operations implies different behaviors, but no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add constructs or relations to the graph. Contradiction detection is automatic.
Operations: add_construct, add_relation.
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes | ||
| face | No | ||
| x | No | ||
| y | No | ||
| question | No | ||
| tags | No | ||
| description | No | ||
| source_id | No | ||
| target_id | No | ||
| relation_type | No | ||
| strength | No | ||
| override_reason | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Notes automatic contradiction detection, which is a behavioral trait. However, with no annotations, the description should disclose more about side effects, reversibility, and required permissions; it only provides one behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus a list of operations make it concise, but it lacks structure and omits important details; every sentence is functional but incomplete.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 12 parameters and no parameter descriptions, the tool is complex. The description does not explain return values (output schema exists) or how to use parameters, leaving significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, yet the description does not explain any of the 12 parameters (e.g., face, x, y, tags, strength). Users must infer meanings from names alone, which is insufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Add constructs or relations to the graph' and lists two operations (add_construct, add_relation). It distinguishes the tool's action from siblings like create_prompt_basis, explore_space, interpret_basis, but doesn't explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Only mentions available operations without context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Interpret a construction basis produced by create_prompt_basis.
Takes the JSON output from create_prompt_basis and returns a plain-language interpretation of the philosophical measurement. Extracts the guidance section and formats it as readable text with dominant dimensions, gaps, and strongest resonance.
| Name | Required | Description | Default |
|---|---|---|---|
| basis | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description details the tool's behavior: extracts the guidance section and formats it as readable text with dominant dimensions, gaps, and strongest resonance. This goes beyond a simple 'interpret' to specify output content.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, no wasted words. Each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter with no schema coverage, the description fully explains the parameter and behavior. Output schema exists, so return value details are not needed. The tool is simple and well-described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'basis' has no schema description, but the description compensates by specifying it expects 'JSON output from create_prompt_basis', adding essential meaning to the raw type string.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'interpret' and the resource 'construction basis produced by create_prompt_basis', effectively differentiating it from sibling tools like create_prompt_basis (which produces the basis).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage context: takes JSON output from create_prompt_basis. While it doesn't explicitly state when not to use alternatives, the dependency on a prior tool output is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.8.0create_prompt_basisexplore_spaceextend_schemainterpret_basisScored across 4 tools
Each tool has a distinct purpose: create measures intent, explore navigates the manifold, extend modifies the graph, interpret explains results. No overlap in functionality.
All tool names follow a consistent verb_noun pattern (create_prompt_basis, explore_space, extend_schema, interpret_basis), making it easy to predict tool roles.
Four tools is an ideal number for a specialized philosophical engine, covering core operations without unnecessary bloat.
The toolkit covers creation, exploration, extension, and interpretation. The only minor gap is the lack of a deletion or reset tool, but this is acceptable for a focused engine.
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