Eine universelle Engine zur Prompt-Erstellung, bereitgestellt als MCP-Server. Sie misst die Absicht anhand von 12 philosophischen Dimensionen und liefert eine Konstruktionsgrundlage, aus der der Client Prompts erstellt.
Die Engine generiert keine Prompts. Sie liefert das dimensionale Fundament – aktive Konstrukte, Spektrum-Gegensätze, Spannungen, Gems, Spokes, Harmonisierungspaare und Konstruktionsfragen –, die die Prompt-Erstellung prinzipienbasiert statt heuristisch machen.
# Install
pip install advanced-prompting-engine
# Or run directly via uvx
uvx advanced-prompting-engineFügen Sie dies zu Ihrer .mcp.json hinzu:
{
"mcpServers": {
"advanced-prompting-engine": {
"command": "uvx",
"args": ["advanced-prompting-engine"]
}
}
}Related MCP server: MCP Prompt Optimizer
Die Engine positioniert Ihre Absicht in einer 12-dimensionalen philosophischen Mannigfaltigkeit:
Fläche | Sub-Dimensionen | Phase |
Ontologie | Partikular ↔ Universal, Statisch ↔ Dynamisch | Verständnis |
Epistemologie | Empirisch ↔ Rational, Gewiss ↔ Vorläufig | Verständnis |
Axiologie | Absolut ↔ Relativ, Quantitativ ↔ Qualitativ | Verständnis |
Teleologie | Unmittelbar ↔ Ultimativ, Intentional ↔ Emergent | Verständnis |
Phänomenologie | Objektiv ↔ Subjektiv, Oberfläche ↔ Tiefe | Verständnis |
Ethik | Deontologisch ↔ Konsequentialistisch, Akteur ↔ Akt | Bewertung |
Ästhetik | Autonom ↔ Kontextuell, Sensorisch ↔ Konzeptuell | Bewertung |
Praxeologie | Individuell ↔ Koordiniert, Reaktiv ↔ Proaktiv | Anwendung |
Methodik | Analytisch ↔ Synthetisch, Deduktiv ↔ Induktiv | Anwendung |
Semiotik | Explizit ↔ Implizit, Syntaktisch ↔ Semantisch | Anwendung |
Hermeneutik | Literal ↔ Figürlich, Autorenabsicht ↔ Leser-Reaktion | Anwendung |
Heuristik | Systematisch ↔ Intuitiv, Konservativ ↔ Explorativ | Anwendung |
Jede Fläche ist ein 12x12-Gitter aus 144 epistemischen Beobachtungspunkten. Die Position bestimmt die Klassifizierung (Ecke/Mittelpunkt/Kante/Zentrum), die Potenz und die Spektrum-Zugehörigkeit. Die 12 Flächen sind als 6 komplementäre Paare (Würfelmodell) organisiert, mit Harmonisierung durch geteilte Oberflächen. Die Engine berechnet Spannungen über Positionskorrespondenz, Gems (Integrationen zwischen den Flächen) mit Würfelebenen-Modulation, Spokes (Verhaltenssignaturen pro Fläche) und einen zentralen Gem-Kohärenzwert.
Tool | Zweck |
| Primär — Absicht oder Koordinate rein, Konstruktionsgrundlage raus |
| Interpretation — Klartext-Lesart einer Konstruktionsgrundlage |
| Experte — Graph-Traversierung, Stresstests, Triangulation |
| Autorenschaft — Hinzufügen von Konstrukten und Beziehungen mit Widerspruchserkennung |
create_prompt_basis(intent="Design an ethical framework for autonomous vehicle decision-making")Die Engine lokalisiert diese Absicht über alle 12 philosophischen Dimensionen hinweg und liefert:
{
"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"
}
}Die Ausgabe sagt Ihnen: Diese Absicht dreht sich primär um Wissensvalidierung (Epistemologie 0,76), welche Entitäten existieren (Ontologie 0,73) und Handlungsstruktur (Praxeologie 0,72). Ethik registriert sich bei 0,53 — vorhanden, aber nicht dominant. Die Harmonisierung zeigt, dass Ontologie und Praxeologie stark resonieren (0,15) — das theoretische „Was existiert“ stimmt mit dem praktischen „Wie man handelt“ überein.
Für präzise Kontrolle übergeben Sie eine Koordinate direkt:
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)Stack: Python + NetworkX (Topologie) + numpy (Berechnung) + SQLite (Persistenz) + MCP SDK
Graph: 1873 Knoten, 2279 Kanten (12 Flächen × 144 Konstrukte + 132 Nexi + 1 zentrales Gem)
Pipeline: 8 Stufen (Absichtsparser → Koordinatenauflöser → Positionsberechner → Konstruktauflöser → Spannungsanalysator → Nexus/Gem-Analysator → Spoke-Analysator → Konstruktionsbrücke)
Geometrie: Vektorgleichgewicht (Kuboktaeder) als latente Topologie zwischen den Flächen, Würfelmodell für 6 komplementäre Paare
Bereitstellung: Einzelprozess, stdio-Transport, kein Daemon, keine externen Abhängigkeiten
docs/DESIGN.md — Vollständige Designspezifikation
docs/CONSTRUCT-v2.md — Die Konstrukt-Spezifikation (was Flächen, Punkte, Spektren, Nexi, Gems, Spokes SIND)
docs/CONSTRUCT-v2-questions.md — 144 Vorlagen für Konstruktionsfragen nach Zone
docs/adr/ — 13 Architektur-Entscheidungsdatensätze
pip install -e ".[dev]"
pytest tests/ -vDas ausgelieferte Paket enthält vorberechnete BGE-abgeleitete Artefakte (semantic_bridge.npz, semantic_vocab.json). Um diese von Grund auf neu zu erstellen (z. B. nach Änderungen an Pol-Synonymen), installieren Sie die Build-Extras:
pip install -e ".[build]"
python -m nltk.downloader wordnet omw-1.4
python scripts/build_semantic_bridge.pyDer Build verwendet BAAI/bge-large-en-v1.5 (~1,3 GB, einmalig in den HuggingFace-Cache heruntergeladen) und wordfreq für die Frequenzsortierung. Laufzeitabhängigkeiten sind davon nicht betroffen — Endbenutzer erhalten nur die vorberechneten Artefakte.
Siehe CONTRIBUTING.md für die Entwicklungseinrichtung und Richtlinien.
Siehe SECURITY.md für Anweisungen zur Meldung von Schwachstellen.
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.
MCP server for generating rough-draft project plans from natural-language prompts.
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