MCP-сервер (Model Context Protocol), реализующий Unified Cognitive Processing Framework (UCPF) для продвинутого когнитивного анализа, творческого решения задач и структурированного мышления.
Сервер DeepLucid3D UCPF — это реализация Unified Cognitive Processing Framework в виде MCP-сервера. Он объединяет рекурсивное самосознание с многомерной категоризацией знаний, обеспечивая мощный подход к решению задач и творческому мышлению.
Этот сервер расширяет возможности ИИ, предоставляя структурированные когнитивные инструменты, которые помогают:
Оценивать когнитивные состояния
Картировать измерения знаний
Применять рекурсивные самовопросы
Генерировать творческие перспективы
Декомпозировать и реинтегрировать сложные проблемы
Related MCP server: Advanced Reasoning MCP Server
Сервер UCPF обеспечивает продвинутую когнитивную обработку с помощью нескольких ключевых функций:
Оценка когнитивного состояния: Определяет текущие когнитивные состояния (Темная инерция, Страсть или Приближение к ясности) для улучшения самосознания в процессе решения задач.
Картирование измерений знаний: Отображает знания в трех измерениях:
Осознанность (Известное vs Неизвестное)
Содержание (Знания vs Незнания)
Доступность (Познаваемое vs Непознаваемое)
Рекурсивные самовопросы: Ставит под сомнение первоначальные предположения и выявляет потенциальные когнитивные искажения.
Генерация творческих перспектив: Создает новые точки зрения и метафорическое мышление для вдохновения на новые решения.
Декомпозиция проблем: Разбивает сложные проблемы на управляемые компоненты и реинтегрирует их с учетом всей системы в целом.
Опциональное управление состоянием: Поддерживает контекст между сессиями для непрерывного анализа.
Node.js (v14 или выше)
npm (v6 или выше)
Среда, совместимая с Model Context Protocol
Клонируйте репозиторий
git clone https://github.com/yourusername/DeepLucid3D-UCPF-Server.git
cd DeepLucid3D-UCPF-ServerУстановите зависимости
npm installСоберите проект
npm run buildНастройте параметры MCP
Добавьте сервер в файл настроек MCP. Для Claude/Cline он обычно находится здесь:
Для Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
Для VSCode Cline: ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json (Linux)
Добавьте следующую конфигурацию:
{
"mcpServers": {
"ucpf": {
"command": "node",
"args": ["path/to/DeepLucid3D-MCP/build/index.js"],
"env": {},
"disabled": false,
"autoApprove": []
}
}
}Перезапустите ваше MCP-совместимое приложение (Claude, VSCode с Cline и т.д.)
Сервер предоставляет три основных инструмента и несколько ресурсов, доступных через MCP:
analyze_problem: Обработка постановки задачи через полную структуру UCPF
<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: Генерация новых перспектив и связей для темы
<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: Управление состоянием для обработки UCPF
<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>Доступ к документации по фреймворку:
<access_mcp_resource>
<server_name>ucpf</server_name>
<uri>ucpf://framework/overview</uri>
</access_mcp_resource>Доступные ресурсы:
ucpf://framework/overview: Обзор фреймворка UCPF
ucpf://framework/cognitive-states: Руководство по когнитивным состояниям
ucpf://framework/knowledge-dimensions: Справочник по измерениям знаний
ucpf://session/{sessionId}/analysis: Результаты анализа для конкретной сессии
Давайте решим классическую загадку: «Кто ходит утром на четырех ногах, днем на двух, а вечером на трех?»
<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>Анализ предоставит:
Оценку когнитивного состояния (вероятно, выявит потенциальное метафорическое мышление)
Картирование знаний о том, что мы знаем и чего не знаем
Рекурсивные вопросы для проверки первоначальных предположений (например, «Предполагаем ли мы буквально ноги?»)
Структурированные перспективы по различным интерпретациям
<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>Это исследование может выявить:
Метафорическую интерпретацию «ног» как опорных конструкций
Метафорическую интерпретацию времени суток как этапов жизни
Что приведет к классическому ответу: человек, который ползает на четырех конечностях в младенчестве, ходит на двух ногах во взрослом возрасте и использует трость (третья «нога») в старости
<access_mcp_resource>
<server_name>ucpf</server_name>
<uri>ucpf://session/sphinx-riddle/analysis</uri>
</access_mcp_resource>Это обеспечит полный путь анализа, показывая, как фреймворк привел к решению через структурированную когнитивную обработку.
Этот проект стоит на плечах гигантов:
Команде Model Context Protocol (MCP) за создание фундаментального протокола, который позволяет ИИ-системам получать доступ к внешним инструментам и ресурсам
Команде Anthropic Claude за их работу над продвинутыми ИИ-системами, способными использовать MCP
Авторам концепций Unified Cognitive Processing Framework, которые лежат в основе методологии когнитивного анализа
Сообществу с открытым исходным кодом, чьи библиотеки и инструменты делают возможными подобные проекты
Лицензия MIT
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 Сервер DeepLucid3D UCPF
TranscriptionTools-MCP — Обработка транскриптов
DeepLucid3D-MCP — Когнитивная обработка
UNO-MCP — Улучшение повествования
gitea-mcp — Интеграция с Gitea
zero-vector-MCP — Процедурная генерация
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.
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