Ein Model Context Protocol (MCP) Server, der das Unified Cognitive Processing Framework (UCPF) für fortgeschrittene kognitive Analysen, kreative Problemlösung und strukturiertes Denken implementiert.
Der DeepLucid3D UCPF Server ist eine Implementierung des Unified Cognitive Processing Framework als MCP-Server. Er kombiniert rekursive Selbstwahrnehmung mit dimensionaler Wissenskategorisierung, um einen leistungsstarken Ansatz für Problemlösung und kreatives Denken zu bieten.
Dieser Server erweitert die KI-Fähigkeiten durch die Bereitstellung strukturierter kognitiver Werkzeuge, die dabei helfen:
Kognitive Zustände zu bewerten
Wissensdimensionen abzubilden
Rekursive Selbstbefragung anzuwenden
Kreative Perspektiven zu generieren
Komplexe Probleme zu zerlegen und wieder zusammenzuführen
Related MCP server: Advanced Reasoning MCP Server
Der UCPF-Server ermöglicht fortgeschrittene kognitive Verarbeitung durch mehrere Schlüsselfunktionen:
Bewertung des kognitiven Zustands: Identifiziert aktuelle kognitive Zustände (Dunkle Trägheit, Leidenschaft oder Annäherung an Klarheit), um die Selbstwahrnehmung während der Problemlösung zu verbessern.
Abbildung von Wissensdimensionen: Bildet Wissen über drei Dimensionen ab:
Bewusstsein (Bekannt vs. Unbekannt)
Inhalt (Bekanntes vs. Unbekanntes)
Zugänglichkeit (Erkennbar vs. Unerkennbar)
Rekursive Selbstbefragung: Hinterfragt anfängliche Annahmen und identifiziert potenzielle kognitive Verzerrungen.
Generierung kreativer Perspektiven: Erzeugt neuartige Sichtweisen und metaphorisches Denken, um neue Lösungen zu inspirieren.
Problemzerlegung: Zerlegt komplexe Probleme in handhabbare Komponenten und führt sie unter Berücksichtigung des Gesamtsystems wieder zusammen.
Optionale Zustandsverwaltung: Behält den Kontext zwischen Sitzungen für laufende Analysen bei.
Node.js (v14 oder höher)
npm (v6 oder höher)
Eine Umgebung, die mit dem Model Context Protocol kompatibel ist
Repository klonen
git clone https://github.com/yourusername/DeepLucid3D-UCPF-Server.git
cd DeepLucid3D-UCPF-ServerAbhängigkeiten installieren
npm installProjekt bauen
npm run buildMCP-Einstellungen konfigurieren
Fügen Sie den Server zu Ihrer MCP-Einstellungsdatei hinzu. Für Claude/Cline befindet sich diese normalerweise unter:
Für Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
Für VSCode Cline: ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json (Linux)
Fügen Sie die folgende Konfiguration hinzu:
{
"mcpServers": {
"ucpf": {
"command": "node",
"args": ["path/to/DeepLucid3D-MCP/build/index.js"],
"env": {},
"disabled": false,
"autoApprove": []
}
}
}Starten Sie Ihre MCP-kompatible Anwendung neu (Claude, VSCode mit Cline, etc.)
Der Server stellt drei Hauptwerkzeuge und mehrere Ressourcen bereit, auf die über MCP zugegriffen werden kann:
analyze_problem: Verarbeitet eine Problemstellung durch das vollständige UCPF-Framework
<use_mcp_tool>
<server_name>ucpf</server_name>
<tool_name>analyze_problem</tool_name>
<arguments>
{
"problem": "Your problem statement here",
"session_id": "optional-session-id",
"enable_state": false,
"detailed": false
}
</arguments>
</use_mcp_tool>creative_exploration: Generiert neuartige Perspektiven und Verbindungen für ein Thema
<use_mcp_tool>
<server_name>ucpf</server_name>
<tool_name>creative_exploration</tool_name>
<arguments>
{
"topic": "Your topic here",
"constraints": ["Optional constraint 1", "Optional constraint 2"],
"perspective_count": 3,
"include_metaphors": true
}
</arguments>
</use_mcp_tool>manage_state: Steuert die Zustandsverwaltung für die UCPF-Verarbeitung
<use_mcp_tool>
<server_name>ucpf</server_name>
<tool_name>manage_state</tool_name>
<arguments>
{
"action": "enable", // or "disable", "reset", "status"
"session_id": "optional-specific-session-id"
}
</arguments>
</use_mcp_tool>Zugriff auf die Framework-Dokumentation:
<access_mcp_resource>
<server_name>ucpf</server_name>
<uri>ucpf://framework/overview</uri>
</access_mcp_resource>Verfügbare Ressourcen:
ucpf://framework/overview: Überblick über das UCPF-Framework
ucpf://framework/cognitive-states: Leitfaden zu kognitiven Zuständen
ucpf://framework/knowledge-dimensions: Referenz für Wissensdimensionen
ucpf://session/{sessionId}/analysis: Analyseergebnisse für eine bestimmte Sitzung
Lösen wir das klassische Rätsel: "Was geht am Morgen auf vier Beinen, am Mittag auf zwei Beinen und am Abend auf drei Beinen?"
<use_mcp_tool>
<server_name>ucpf</server_name>
<tool_name>manage_state</tool_name>
<arguments>
{
"action": "enable"
}
</arguments>
</use_mcp_tool><use_mcp_tool>
<server_name>ucpf</server_name>
<tool_name>analyze_problem</tool_name>
<arguments>
{
"problem": "What walks on four legs in the morning, two legs in the afternoon, and three legs in the evening?",
"session_id": "sphinx-riddle",
"enable_state": true
}
</arguments>
</use_mcp_tool>Die Analyse liefert:
Bewertung des kognitiven Zustands (identifiziert wahrscheinlich potenzielles metaphorisches Denken)
Wissenskartierung dessen, was wir wissen und nicht wissen
Rekursive Fragen, um anfängliche Annahmen zu hinterfragen (z. B. "Gehen wir von wörtlichen Beinen aus?")
Strukturierte Perspektiven zu verschiedenen Interpretationen
<use_mcp_tool>
<server_name>ucpf</server_name>
<tool_name>creative_exploration</tool_name>
<arguments>
{
"topic": "Walking with different numbers of legs at different times of day",
"constraints": ["morning", "afternoon", "evening", "four", "two", "three"],
"include_metaphors": true,
"session_id": "sphinx-riddle"
}
</arguments>
</use_mcp_tool>Diese Erkundung könnte Folgendes offenbaren:
Die metaphorische Interpretation von "Beinen" als Stützstrukturen
Die metaphorische Interpretation von Tageszeiten als Lebensphasen
Führt zur klassischen Antwort: ein Mensch, der als Baby auf vier Gliedmaßen krabbelt, als Erwachsener auf zwei Beinen geht und im Alter einen Gehstock (drittes "Bein") benutzt
<access_mcp_resource>
<server_name>ucpf</server_name>
<uri>ucpf://session/sphinx-riddle/analysis</uri>
</access_mcp_resource>Dies bietet den vollständigen Analyseweg und zeigt, wie das Framework durch strukturierte kognitive Verarbeitung zur Lösung geführt hat.
Dieses Projekt steht auf den Schultern von Giganten:
Dem Model Context Protocol (MCP)-Team für die Schaffung des grundlegenden Protokolls, das es KI-Systemen ermöglicht, auf externe Werkzeuge und Ressourcen zuzugreifen
Dem Anthropic Claude-Team für ihre Arbeit an fortschrittlichen KI-Systemen, die in der Lage sind, MCP zu nutzen
Den Mitwirkenden an den Konzepten des Unified Cognitive Processing Framework, die die Methodik der kognitiven Analyse antreiben
Der Open-Source-Community, deren Bibliotheken und Werkzeuge Projekte wie dieses ermöglichen
MIT-Lizenz
DeepLucid3D-UCPF-Server/
├── src/
│ ├── engine/
│ │ ├── ucpf-core.ts # Core UCPF processing logic
│ │ ├── creative-patterns.ts # Creative thinking utilities
│ │ └── state-manager.ts # Session state management
│ ├── tools/
│ │ ├── analyze-problem.ts # Problem analysis tool
│ │ └── creative-exploration.ts # Creative exploration tool
│ └── index.ts # Main server implementation
├── build/ # Compiled JavaScript files
├── package.json # Project dependencies and scripts
└── README.md # This documentation© 2025 DeepLucid3D UCPF Server
TranscriptionTools-MCP — Transkriptverarbeitung
DeepLucid3D-MCP — Kognitive Verarbeitung
UNO-MCP — Narrative Erweiterung
gitea-mcp — Gitea-Integration
zero-vector-MCP — Prozedurale Generierung
Process a problem statement through the full UCPF framework
| Name | Required | Description | Default |
|---|---|---|---|
| detailed | No | Whether to include detailed analysis | |
| enable_state | No | Whether to enable state management for this analysis | |
| problem | Yes | The problem statement to analyze | |
| session_id | No | Optional session ID for maintaining state between calls |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'process' and 'full UCPF framework', implying analysis or transformation, but doesn't describe what the tool actually does behaviorally—such as whether it performs computations, returns structured data, has side effects, or requires specific permissions. This leaves significant gaps in understanding the tool's operation and impact.
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?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states the action and resource, making it easy to parse and understand quickly, with no wasted information.
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 the complexity implied by 'full UCPF framework' and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how it handles state or detailed analysis, or its behavioral traits, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.
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 100%, so the schema fully documents all four parameters. The description adds no additional meaning beyond what the schema provides, such as explaining how parameters interact with the UCPF framework or typical use cases. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
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 states the tool 'processes a problem statement through the full UCPF framework', which provides a clear verb ('process') and resource ('problem statement') with a specific methodology ('UCPF framework'). However, it doesn't differentiate from sibling tools like 'creative_exploration' or 'manage_state', leaving the specific scope of 'full UCPF framework' somewhat vague compared to alternatives.
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 provides no guidance on when to use this tool versus the sibling tools 'creative_exploration' or 'manage_state'. It lacks explicit context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name and description alone without clear alternatives or constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate novel perspectives and connections for a topic
| Name | Required | Description | Default |
|---|---|---|---|
| constraints | No | Optional constraints or parameters to consider | |
| include_metaphors | No | Whether to include metaphorical thinking | |
| perspective_count | No | Number of perspectives to generate | |
| session_id | No | Optional session ID for maintaining state between calls | |
| topic | Yes | The topic or problem to explore creatively |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions generating 'novel perspectives and connections,' which implies a creative, non-destructive process, but fails to detail aspects like rate limits, authentication needs, output format, or whether it maintains state (e.g., via session_id). For a tool with no annotations, this is a significant gap in transparency.
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?
The description is a single, efficient sentence: 'Generate novel perspectives and connections for a topic.' It is front-loaded with the core action and resource, with no wasted words. This makes it easy to parse and understand quickly.
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 the tool's complexity (5 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits, output format, and how parameters interact, which are crucial for an AI agent to use it effectively. Without annotations or an output schema, the description should provide more context to compensate, but it does not.
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 100%, meaning all parameters are documented in the schema. The description adds no additional meaning beyond what the schema provides, such as explaining how 'constraints' affect generation or what 'perspective_count' entails. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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's function: 'Generate novel perspectives and connections for a topic.' It specifies the verb ('generate') and resource ('perspectives and connections'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze_problem' or 'manage_state', which might also involve topic exploration or state management.
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 provides no guidance on when to use this tool versus alternatives. There is no mention of when to choose 'creative_exploration' over 'analyze_problem' or 'manage_state', nor any context about prerequisites or exclusions. This leaves the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Control the state management for UCPF processing
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | The state management action to perform | |
| session_id | No | Optional session ID to target a specific session |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'control' and actions like 'enable', 'disable', 'reset', and 'status', implying mutation capabilities, but doesn't specify permissions needed, side effects (e.g., data loss on reset), rate limits, or response format. This leaves critical behavioral traits undocumented for a tool that appears to modify system state.
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?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a tool with two parameters, though it could be more front-loaded with key details given the lack of annotations and output schema.
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 the complexity implied by state management actions (including mutations like 'disable' and 'reset'), no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral risks, response expectations, or error conditions, leaving significant gaps for the agent to operate safely and effectively.
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 100%, with clear documentation for both parameters (action with enum values and optional session_id). The description adds no additional parameter semantics beyond what the schema provides, such as explaining what 'reset' entails or when to use session_id. This meets the baseline of 3 since the schema does the heavy lifting.
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 states the tool 'Control[s] the state management for UCPF processing', which provides a general purpose (state management) and domain (UCPF processing) but lacks specificity about what 'state management' entails or what resources are affected. It doesn't distinguish from sibling tools like 'analyze_problem' or 'creative_exploration', leaving the agent to infer differences based on tool names alone.
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 is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, typical scenarios, or exclusions, and there's no comparison to sibling tools. The agent must rely solely on the tool name and input schema to infer usage context.
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.
v1.0.0analyze_problemcreative_explorationmanage_stateScored across 3 tools
Each tool has a clearly distinct purpose: analyze_problem handles problem statement processing, creative_exploration generates novel perspectives, and manage_state controls state management. There is no overlap in functionality, making tool selection straightforward for an agent.
The tools follow a consistent verb_noun pattern (analyze_problem, creative_exploration, manage_state), which is predictable and readable. The minor deviation is that 'manage_state' uses a verb_noun structure while the others are adjective_noun or verb_noun, but overall consistency is maintained.
With only 3 tools, the set feels thin for a server named 'DeepLucid3D UCPF Server,' which suggests a complex framework. While each tool seems essential, the low count may limit coverage of the UCPF domain, potentially requiring agents to work around missing operations.
Inferred domain is UCPF (Unified Creative Problem Framework) processing, but the tool set has significant gaps. It lacks core operations like retrieving results, updating analyses, or deleting states, which could lead to agent failures in handling full problem-solving lifecycles. The surface is incomplete for the stated purpose.
MCP server for progressive tool usage at any scale (see https://klavis.ai)
An MCP server for deep research or task groups
MCP server exposing Kettle Logic insight articles & industry guidance as tools + resources.
MCP server for generating rough-draft project plans from natural-language prompts.
