고급 인지 분석, 창의적 문제 해결 및 구조화된 사고를 위한 통합 인지 처리 프레임워크(UCPF)를 구현하는 모델 컨텍스트 프로토콜(MCP) 서버입니다.
DeepLucid3D UCPF 서버는 MCP 서버로서 통합 인지 처리 프레임워크를 구현한 것입니다. 재귀적 자기 인식과 차원적 지식 분류를 결합하여 문제 해결과 창의적 사고에 강력한 접근 방식을 제공합니다.
이 서버는 다음과 같은 도움을 주는 구조화된 인지 도구를 제공하여 AI 기능을 확장합니다:
인지 상태 평가
지식 차원 매핑
재귀적 자기 질문 적용
창의적 관점 생성
복잡한 문제 분해 및 재통합
Related MCP server: Advanced Reasoning MCP Server
UCPF 서버는 다음과 같은 몇 가지 핵심 기능을 통해 고급 인지 처리를 가능하게 합니다:
인지 상태 평가: 문제 해결 과정에서 자기 인식을 향상하기 위해 현재의 인지 상태(어둠의 관성, 열정 또는 명료함에 접근)를 식별합니다.
지식 차원 매핑: 세 가지 차원에 걸쳐 지식을 매핑합니다:
인식 (알려진 것 vs 알려지지 않은 것)
내용 (아는 것 vs 모르는 것)
접근성 (알 수 있는 것 vs 알 수 없는 것)
재귀적 자기 질문: 초기 가정을 검토하고 잠재적인 인지 편향을 식별합니다.
창의적 관점 생성: 새로운 해결책을 고무하기 위해 참신한 관점과 은유적 사고를 생성합니다.
문제 분해: 복잡한 문제를 관리 가능한 구성 요소로 나누고 전체 시스템에 대한 인식과 함께 재통합합니다.
선택적 상태 관리: 지속적인 분석을 위해 세션 간 컨텍스트를 유지합니다.
Node.js (v14 이상)
npm (v6 이상)
모델 컨텍스트 프로토콜과 호환되는 환경
저장소 복제
git clone https://github.com/yourusername/DeepLucid3D-UCPF-Server.git
cd DeepLucid3D-UCPF-Server의존성 설치
npm install프로젝트 빌드
npm run buildMCP 설정 구성
MCP 설정 파일에 서버를 추가합니다. Claude/Cline의 경우 일반적으로 다음 위치에 있습니다:
Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
VSCode Cline: ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json (Linux)
다음 구성을 추가합니다:
{
"mcpServers": {
"ucpf": {
"command": "node",
"args": ["path/to/DeepLucid3D-MCP/build/index.js"],
"env": {},
"disabled": false,
"autoApprove": []
}
}
}MCP 호환 애플리케이션 재시작 (Claude, Cline이 포함된 VSCode 등)
이 서버는 MCP를 통해 액세스할 수 있는 세 가지 주요 도구와 여러 리소스를 제공합니다:
analyze_problem: 전체 UCPF 프레임워크를 통해 문제 진술을 처리합니다
<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: 주제에 대한 참신한 관점과 연결을 생성합니다
<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: UCPF 처리를 위한 상태 관리를 제어합니다
<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>프레임워크 문서에 액세스:
<access_mcp_resource>
<server_name>ucpf</server_name>
<uri>ucpf://framework/overview</uri>
</access_mcp_resource>사용 가능한 리소스:
ucpf://framework/overview: UCPF 프레임워크 개요
ucpf://framework/cognitive-states: 인지 상태 가이드
ucpf://framework/knowledge-dimensions: 지식 차원 참조
ucpf://session/{sessionId}/analysis: 특정 세션에 대한 분석 결과
고전적인 수수께끼를 풀어봅시다: "아침에는 네 발로 걷고, 오후에는 두 발로 걷고, 저녁에는 세 발로 걷는 것은 무엇인가?"
<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>분석 결과는 다음을 제공합니다:
인지 상태 평가 (잠재적인 은유적 사고 식별 가능)
우리가 아는 것과 모르는 것에 대한 지식 매핑
초기 가정을 검토하기 위한 재귀적 질문 (예: "우리가 문자 그대로의 다리를 가정하고 있는가?")
다양한 해석에 대한 구조화된 관점
<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>이 탐색을 통해 다음을 발견할 수 있습니다:
"다리"를 지지 구조로 해석하는 은유적 해석
하루의 시간을 인생의 단계로 해석하는 은유적 해석
고전적인 정답으로 이어짐: 아기일 때는 네 발로 기고, 성인이 되면 두 발로 걷고, 노년에는 지팡이(세 번째 "다리")를 사용하는 인간
<access_mcp_resource>
<server_name>ucpf</server_name>
<uri>ucpf://session/sphinx-riddle/analysis</uri>
</access_mcp_resource>이는 프레임워크가 구조화된 인지 처리를 통해 어떻게 해결책에 도달했는지 보여주는 전체 분석 과정을 제공합니다.
이 프로젝트는 거인들의 어깨 위에 서 있습니다:
AI 시스템이 외부 도구와 리소스에 액세스할 수 있게 하는 기반 프로토콜을 만든 모델 컨텍스트 프로토콜(MCP) 팀
MCP를 활용할 수 있는 고급 AI 시스템을 연구하는 Anthropic Claude 팀
인지 분석 방법론을 뒷받침하는 통합 인지 처리 프레임워크 개념에 기여한 분들
이와 같은 프로젝트를 가능하게 하는 라이브러리와 도구를 제공한 오픈 소스 커뮤니티
MIT 라이선스
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 서버
TranscriptionTools-MCP — 녹취록 처리
DeepLucid3D-MCP — 인지 처리
UNO-MCP — 서사 강화
gitea-mcp — Gitea 통합
zero-vector-MCP — 절차적 생성
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.
