使用说明
Advanced Prompting Engine
A universal prompt creation engine delivered as an MCP server. Measures intent across 12 philosophical dimensions and returns a construction basis from which the client constructs prompts.
The engine does not generate prompts. It provides the dimensional foundation — active constructs, spectrum opposites, tensions, gems, spokes, harmonization pairs, and construction questions — that make prompt construction principled rather than heuristic.
Quick Start
# Install
pip install advanced-prompting-engine
# Or run directly via uvx
uvx advanced-prompting-engine
MCP Configuration
Add to your .mcp.json:
{
"mcpServers": {
"advanced-prompting-engine": {
"command": "uvx",
"args": ["advanced-prompting-engine"]
}
}
}
What It Does
The engine positions your intent in a 12-dimensional philosophical manifold:
| Face | Sub-dimensions | Phase |
|---|---|---|
| Ontology | Particular ↔ Universal, Static ↔ Dynamic | Comprehension |
| Epistemology | Empirical ↔ Rational, Certain ↔ Provisional | Comprehension |
| Axiology | Absolute ↔ Relative, Quantitative ↔ Qualitative | Comprehension |
| Teleology | Immediate ↔ Ultimate, Intentional ↔ Emergent | Comprehension |
| Phenomenology | Objective ↔ Subjective, Surface ↔ Deep | Comprehension |
| Ethics | Deontological ↔ Consequential, Agent ↔ Act | Evaluation |
| Aesthetics | Autonomous ↔ Contextual, Sensory ↔ Conceptual | Evaluation |
| Praxeology | Individual ↔ Coordinated, Reactive ↔ Proactive | Application |
| Methodology | Analytic ↔ Synthetic, Deductive ↔ Inductive | Application |
| Semiotics | Explicit ↔ Implicit, Syntactic ↔ Semantic | Application |
| Hermeneutics | Literal ↔ Figurative, Author-intent ↔ Reader-response | Application |
| Heuristics | Systematic ↔ Intuitive, Conservative ↔ Exploratory | Application |
Each face is a 12x12 grid of 144 epistemic observation points. Position determines classification (corner/midpoint/edge/center), potency, and spectrum membership. The 12 faces are organized as 6 complementary pairs (cube model) with harmonization through shared surfaces. The engine computes tensions via positional correspondence, gems (inter-face integrations) with cube tier modulation, spokes (per-face behavioral signatures), and a central gem coherence score.
Tools
| Tool | Purpose |
|---|---|
create_prompt_basis |
Primary — intent or coordinate in, construction basis out |
interpret_basis |
Interpretation — plain-language reading of a construction basis |
explore_space |
Expert — graph traversal, stress testing, triangulation |
extend_schema |
Authoring — add constructs and relations with contradiction detection |
Example: Natural Language Intent
create_prompt_basis(intent="Design an ethical framework for autonomous vehicle decision-making")
The engine locates this intent across all 12 philosophical dimensions and returns:
{
"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"
}
}
The output tells you: this intent is primarily about knowledge validation (epistemology 0.76), what entities exist (ontology 0.73), and action structure (praxeology 0.72). Ethics registers at 0.53 — present but not dominant. The harmonization shows ontology and praxeology resonate strongly (0.15) — the theoretical "what exists" aligns with the practical "how to act."
Example: Pre-formed Coordinate
For precise control, pass a coordinate directly:
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)
Architecture
- Stack: Python + NetworkX (topology) + numpy (computation) + SQLite (persistence) + MCP SDK
- Graph: 1873 nodes, 2279 edges (12 faces × 144 constructs + 132 nexi + 1 central gem)
- Pipeline: 8 stages (Intent Parser → Coordinate Resolver → Position Computer → Construct Resolver → Tension Analyzer → Nexus/Gem Analyzer → Spoke Analyzer → Construction Bridge)
- Geometry: Vector Equilibrium (cuboctahedron) as latent inter-face topology, cube model for 6 complementary pairs
- Deployment: Single process, stdio transport, no daemon, no external dependencies
Documentation
docs/DESIGN.md— Full design specificationdocs/CONSTRUCT-v2.md— The Construct specification (what faces, points, spectrums, nexi, gems, spokes ARE)docs/CONSTRUCT-v2-questions.md— 144 construction question templates by zonedocs/adr/— 13 Architecture Decision Records
Development
pip install -e ".[dev]"
pytest tests/ -v
Rebuilding the semantic bridge (optional)
The shipped package includes pre-computed BGE-derived artifacts (semantic_bridge.npz, semantic_vocab.json). To rebuild them from scratch (e.g., after pole-synonym edits), install the build extras:
pip install -e ".[build]"
python -m nltk.downloader wordnet omw-1.4
python scripts/build_semantic_bridge.py
The build uses BAAI/bge-large-en-v1.5 (~1.3 GB, downloaded once to HuggingFace cache) and wordfreq for frequency ordering. Runtime dependencies are unaffected — end users only receive the pre-computed artifacts.
Contributing
See CONTRIBUTING.md for development setup and guidelines.
Security
See SECURITY.md for vulnerability reporting instructions.
License
MIT
How this plugin runs its MCP server (shared background service)
Claude Code normally starts a private copy of a plugin's MCP server for every open session. This
plugin instead runs one shared copy per machine: its MCP entry launches shared_mcp.py connect,
which on first use creates a small Python environment under ~/.local/state/shared-mcp/, registers a
login-time background service (launchd on macOS, systemd --user on Linux, a detached process
elsewhere) that runs the server once and serves it to every session over HTTP on 127.0.0.1 only,
and then connects. Later sessions just connect. The first run prints a one-line notice.
If a background service cannot be set up (no network, no service manager, an unusual OS), the
original server runs directly as before — never a broken plugin. To opt out permanently set
SHARED_MCP_DISABLE=1 in your environment; to remove the service run
python3 <plugin>/shared_mcp.py stop --name advanced-prompting-engine. State, logs and the service definition live under
~/.local/state/shared-mcp/advanced-prompting-engine/. The kit is the single file shared_mcp.py vendored into this plugin; source, tests and design notes: https://github.com/JoshuaRamirez/shared-mcp
