Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Advanced Prompting Engine Create a prompt basis for a Socratic tutor focused on empirical truth"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
# Install
pip install advanced-prompting-engine
# Or run directly via uvx
uvx advanced-prompting-engineAdd to your .mcp.json:
{
"mcpServers": {
"advanced-prompting-engine": {
"command": "uvx",
"args": ["advanced-prompting-engine"]
}
}
}Related MCP server: MCP Prompt Optimizer
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.
Tool | Purpose |
| Primary — intent or coordinate in, construction basis out |
| Interpretation — plain-language reading of a construction basis |
| Expert — graph traversal, stress testing, triangulation |
| Authoring — add constructs and relations with contradiction detection |
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."
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)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
docs/DESIGN.md — Full design specification
docs/CONSTRUCT-v2.md — The Construct specification (what faces, points, spectrums, nexi, gems, spokes ARE)
docs/CONSTRUCT-v2-questions.md — 144 construction question templates by zone
docs/adr/ — 13 Architecture Decision Records
pip install -e ".[dev]"
pytest tests/ -vThe 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.pyThe 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.
See CONTRIBUTING.md for development setup and guidelines.
See SECURITY.md for vulnerability reporting instructions.
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. Dates show when Glama detected each change.
v0.8.0create_prompt_basisexplore_spaceextend_schemainterpret_basisEach 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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