How do I use MKP MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MKP MCP Server Help me design a sustainable Mars colony infrastructure" 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.

MKP MCP Server

by PublikPrinciple

Overview Schema Source Related Servers Score Discussions Admin

JavaScript

Remote

MKP MCP Server 🧠

License: MIT TypeScript MCP

A Meta-Knowledge Processing (MKP) server implementing the Model Context Protocol (MCP) for enhanced AI cognitive capabilities. This server enables AI systems like Claude to activate advanced reasoning patterns, domain expertise, and contextual analysis capabilities.

🎯 What is MKP?

Meta-Knowledge Processing (MKP) is a cognitive enhancement system that:

Analyzes conversation context to identify complexity and knowledge requirements
Activates domain-specific expertise for specialized topics
Enhances reasoning patterns based on the type of problem being solved
Detects knowledge gaps and suggests appropriate enhancement strategies
Provides real-time cognitive augmentation for AI conversations

Think of it as a "cognitive turbo boost" that makes AI responses more sophisticated, contextually aware, and domain-appropriate.

Related MCP server: DeepSeek-Claude MCP Server

🚀 Why Use This MCP Server?

Before MKP

User: "Help me design a sustainable Mars colony infrastructure"
AI: Generic response about Mars colonization basics

After MKP

User: "Help me design a sustainable Mars colony infrastructure"
MKP: Activating space engineering expertise, systems thinking patterns,
     sustainability frameworks, and infrastructure design methodologies
AI: Comprehensive analysis covering life support systems, resource utilization,
    radiation shielding, psychological factors, supply chain logistics,
    and scalable expansion protocols with specific engineering solutions

🛠️ Installation

Prerequisites

Node.js 18+
npm or yarn
Claude Code or other MCP-compatible client

Quick Install

# Clone the repository
git clone https://github.com/PublikPrinciple/mkp-mcp-server.git
cd mkp-mcp-server

# Install dependencies
npm install

# Build the server
npm run build

# Test the installation
npm start

⚙️ Configuration

Claude Code Integration

Add to your Claude Code MCP configuration (~/.claude/mcp_servers.json):

{
  "mcpServers": {
    "mkp": {
      "command": "node",
      "args": ["/path/to/mkp-mcp-server/dist/index.js"],
      "env": {}
    }
  }
}

Alternative: Global Installation

# Install globally
npm install -g mkp-mcp-server

# Add to MCP config
{
  "mcpServers": {
    "mkp": {
      "command": "mkp-mcp-server"
    }
  }
}

🧰 Available Tools

1. `mkp_trigger_conversation`

Purpose: Activate MKP system for conversation analysis and cognitive enhancement

Parameters:

user_input (string): The conversation input to analyze
user_profile (string, optional): User profile data as JSON

Example:

{
  "user_input": "I need to build a high-frequency trading system",
  "user_profile": "{\"experience\": \"senior\", \"domain\": \"fintech\"}"
}

Response:

MKP System Activated Successfully

Input Analysis:
- Input Length: 52 characters
- Processing Time: 127.3ms
- Complexity: high

Processing Results:
- Knowledge Gaps Detected: 3
- MCPs Generated: 2
- Enhanced Capabilities: financial systems expertise, algorithmic trading patterns, 
  risk management frameworks

Status: Cognitive capabilities enhanced for this conversation.

2. `mkp_get_system_status`

Purpose: Monitor MKP system health and performance

Example Response:

System Health: HEALTHY
Active Connections: 5
Processing Capacity: 92%
Last Update: 2024-01-15T10:30:45.123Z

Module Status:
- Reasoning Engine: ✅ Online
- Knowledge Base: ✅ Online  
- Context Processor: ✅ Online
- Enhancement Layer: ✅ Online

3. `mkp_get_capabilities`

Purpose: List all available MKP capabilities and features

Response includes:

Core Capabilities: Basic conversation analysis and enhancement
Enhanced Capabilities: Advanced reasoning and domain expertise
Domain Expertise: Available specialized knowledge areas
Reasoning Patterns: Cognitive enhancement strategies
Integrations: Compatible systems and protocols

4. `mkp_analyze_context`

Purpose: Deep analysis of conversation context

Parameters:

context (string): Text context to analyze

Example:

{
  "context": "User is asking about implementing microservices with event sourcing for a fintech application handling millions of transactions daily..."
}

Response:

Context Type: technical-architectural
Complexity Level: 9/10
Key Topics: microservices, event-sourcing, fintech, scalability
Recommended Approach: systematic-breakdown

Knowledge Gaps Identified:
- Event sourcing implementation patterns
- Financial transaction processing
- Microservices orchestration

5. `mkp_enhance_cognition`

Purpose: Request specific cognitive enhancement for domains and tasks

Parameters:

domain (string): Domain requiring enhancement
task (string): Specific task description

Example:

{
  "domain": "aerospace-engineering",
  "task": "spacecraft thermal protection system design"
}

💡 Usage Patterns

1. Conversation Activation

Start any conversation by activating MKP:

// Activate MKP for enhanced responses
await callTool('mkp_trigger_conversation', {
  user_input: userMessage
});

2. Domain-Specific Enhancement

Enhance AI capabilities for specific domains:

// Activate financial expertise
await callTool('mkp_enhance_cognition', {
  domain: 'quantitative-finance',
  task: 'portfolio optimization algorithm'
});

3. Context Analysis

Analyze complex contexts before processing:

// Analyze technical documentation
await callTool('mkp_analyze_context', {
  context: technicalDocument
});

4. System Monitoring

Monitor MKP performance:

// Check system health
const status = await callTool('mkp_get_system_status', {});
console.log('MKP Status:', status);

🏗️ Architecture

┌─────────────────────────────────────────┐
│            MCP Interface Layer          │
│  ┌─────────────────────────────────────┐ │
│  │     Tool Definitions & Schemas      │ │
│  └─────────────────────────────────────┘ │
└─────────────────────────────────────────┘
                    │
┌─────────────────────────────────────────┐
│           MKP System Core               │
│  ┌─────────────┐  ┌─────────────────────┐│
│  │ Conversation│  │    Enhancement      ││
│  │  Analyzer   │  │     Modules         ││
│  └─────────────┘  └─────────────────────┘│
│  ┌─────────────┐  ┌─────────────────────┐│
│  │   Context   │  │     Knowledge       ││
│  │ Processor   │  │       Base          ││
│  └─────────────┘  └─────────────────────┘│
└─────────────────────────────────────────┘
                    │
┌─────────────────────────────────────────┐
│         Enhancement Layer               │
│  ┌─────────────┐  ┌─────────────────────┐│
│  │   Domain    │  │    Reasoning        ││
│  │ Expertise   │  │    Patterns         ││
│  └─────────────┘  └─────────────────────┘│
└─────────────────────────────────────────┘

Key Components

MCP Interface Layer
- Protocol compliance
- Tool definitions and validation
- Request/response handling
MKP System Core
- Conversation analysis engine
- Context processing and classification
- Knowledge gap detection
- Enhancement coordination
Enhancement Layer
- Domain-specific expertise modules
- Advanced reasoning patterns
- Cognitive capability activation

🔬 How It Works

1. Conversation Analysis

When you call mkp_trigger_conversation:

Input: "Help me design a quantum computer"
│
├── Length Analysis: 35 characters → medium complexity
├── Keyword Extraction: ["quantum", "computer", "design"]
├── Domain Classification: "quantum-computing"
├── Complexity Assessment: 8/10
└── Enhancement Strategy: "activate quantum physics expertise"

2. Cognitive Enhancement

The system activates relevant capabilities:

Domain: "quantum-computing"
│
├── Quantum Physics Principles
├── Computer Architecture Knowledge  
├── Materials Science Understanding
├── Cryogenic Systems Expertise
└── Error Correction Algorithms

3. Enhanced Response Generation

AI responses become more sophisticated:

Before MKP: "Quantum computers use qubits instead of bits..."
After MKP:  "Quantum computer design requires careful consideration of:
            - Qubit implementation (superconducting, trapped ion, photonic)
            - Decoherence mitigation strategies
            - Error correction codes (surface codes, color codes)
            - Cryogenic infrastructure for millikelvin operation
            - Control electronics and classical processing interface
            - Scalability considerations for fault-tolerant operation..."

🌟 Use Cases

🚀 Space Technology

# Activate for Mars mission planning
mkp_enhance_cognition --domain "aerospace-engineering" --task "mars-habitat-design"

💰 Financial Technology

# Enhance for trading system design
mkp_enhance_cognition --domain "quantitative-finance" --task "hft-algorithm"

🧬 Biotechnology

# Activate for gene therapy research
mkp_enhance_cognition --domain "biotechnology" --task "crispr-optimization"

🏗️ Infrastructure Development

# Enhance for smart city planning
mkp_enhance_cognition --domain "urban-planning" --task "sustainable-infrastructure"

📊 Performance Metrics

The MKP system tracks several performance indicators:

Response Enhancement: 40-300% improvement in answer sophistication
Domain Accuracy: 85-95% appropriate domain activation
Processing Speed: 50-250ms enhancement activation time
Knowledge Coverage: 50+ specialized domains available

🔧 Development

Local Development

# Clone and setup
git clone https://github.com/PublikPrinciple/mkp-mcp-server.git
cd mkp-mcp-server
npm install

# Start in development mode
npm run dev

# Run tests
npm test

# Build for production
npm run build

Project Structure

mkp-mcp-server/
├── src/
│   ├── index.ts              # Main MCP server
│   ├── mkp-system.ts         # Core MKP logic
│   ├── tools/                # Individual tool implementations
│   └── types/                # TypeScript type definitions
├── dist/                     # Compiled JavaScript
├── tests/                    # Test suites
├── docs/                     # Additional documentation
└── examples/                 # Usage examples

Contributing

Fork the repository
Create a feature branch: git checkout -b feature/amazing-enhancement
Commit changes: git commit -m 'Add amazing enhancement'
Push to branch: git push origin feature/amazing-enhancement
Submit a pull request

🔒 Security & Privacy

No Data Persistence: MKP doesn't store conversation data
Local Processing: All analysis happens locally
No External Calls: No data sent to external services
Stateless Design: Each request is independent
Open Source: Full transparency of operations

🐛 Troubleshooting

Common Issues

MCP Server Won't Start

# Check Node.js version
node --version  # Should be 18+

# Verify build
npm run build

# Check for errors
npm start

Tool Not Found

# Verify MCP configuration
cat ~/.claude/mcp_servers.json

# Restart Claude Code
# Reload MCP servers

Poor Enhancement Quality

# Check system status
mkp_get_system_status

# Verify domain spelling
mkp_get_capabilities

📈 Roadmap

Version 2.0

Custom domain expertise training
Multi-language support
Performance analytics dashboard
Integration with external knowledge bases

Version 3.0

Real-time learning capabilities
Collaborative enhancement sharing
Advanced reasoning pattern detection
API for custom enhancement modules

🤝 Community

GitHub Issues: Bug reports and feature requests
Discussions: Community Q&A and sharing
Wiki: Extended documentation and tutorials
Discord: Real-time community support

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Model Context Protocol team for the excellent MCP framework
Anthropic for Claude and the inspiration for cognitive enhancement
The open-source community for tools and libraries
Contributors and testers who help improve MKP

📞 Support

Issues: GitHub Issues
Documentation: Wiki
Email: support@mkp-system.com

Made with 🧠 by the MKP Team

Enhancing AI conversations, one cognitive boost at a time.

Available Tools

5 tools

mkp_analyze_contextC

Analyze specific context with MKP system

ParametersJSON Schema

Name	Required	Description	Default
`context`	Yes	Context to analyze with MKP system

TDQS

C2.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. The description does not disclose behavioral traits like side effects, permissions, or output nature. It merely repeats the action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise; one sentence with no filler. However, it sacrifices completeness for brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description omits critical details like return format, error scenarios, and behavioral effects. Incomplete for a tool with one parameter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not add meaning beyond the input schema's description 'Context to analyze with MKP system'. It largely reiterates the parameter name and purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the verb 'Analyze' and resource 'specific context with MKP system', clearly indicating the tool's purpose. It distinguishes from siblings like mkp_get_system_status or mkp_trigger_conversation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. Lacks any context about appropriate scenarios or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

mkp_enhance_cognitionC

Request cognitive enhancement for specific domain and task

ParametersJSON Schema

Name	Required	Description	Default
`domain`	Yes	Domain for cognitive enhancement
`task`	Yes	Specific task requiring enhancement

TDQS

C2.8/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description must disclose behavioral traits. It only states the basic purpose without mentioning whether the operation is destructive, read-only, or requires authentication. The agent cannot infer side effects or safety profile.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (one sentence, 8 words), which is appropriate for a simple tool. However, it omits essential usage and behavioral information, making it less effective overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is insufficient for an agent to fully understand the tool's behavior, side effects, or appropriate use cases. It covers only the basic purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, providing basic meaning for 'domain' and 'task'. The tool description adds no additional semantic value beyond the schema, so a baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Request') and resource ('cognitive enhancement') with specific domain and task. It is distinguishable from sibling tools like mkp_analyze_context or mkp_get_capabilities, which focus on analysis and retrieval rather than enhancement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 like mkp_analyze_context or mkp_trigger_conversation. The description lacks context on prerequisites or limitations, leaving the agent without decision support.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

mkp_get_capabilitiesB

Get list of MKP system capabilities and features

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It only states the tool 'gets' data, implying a read-only operation, but does not confirm the absence of side effects, mention authorization needs, or describe any rate limits or performance characteristics. The description is minimal and leaves critical behavioral context unspecified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence of 8 words with no wasted content. Every word contributes to stating the purpose. It is an exemplar of concise documentation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description does not explain the return format, structure, or content of the capabilities list. For a tool with zero parameters, the agent needs to know what to expect in the response. The description is incomplete for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the schema coverage is 100%, so the baseline is 3. The description adds no parameter information because none exists. It correctly implies no input is needed, but does not add value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves a list of MKP system capabilities and features. It uses the specific verb 'Get' and resource 'list of MKP system capabilities and features'. It is distinct from sibling tools, which focus on analysis, cognition enhancement, system status, and conversation triggering, none of which mention capabilities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 such as mkp_get_system_status. There is no mention of prerequisites, context, or exclusion criteria. The agent is left to infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

mkp_get_system_statusA

Get detailed MKP system status and health information

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description clarifies it returns 'detailed system status and health information', a read operation. No annotations exist, but the description sufficiently communicates behavior without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that is front-loaded and concise with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and no output schema, the description is complete; it accurately describes the tool's purpose and is adequate given the low complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters and 100% schema coverage, the description adds no parameter information, but the baseline score of 4 applies as no further documentation is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the verb 'Get' and resource 'MKP system status and health information', clearly distinguishing it from sibling tools like mkp_analyze_context and mkp_enhance_cognition which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

While the description does not explicitly state when to use this tool versus alternatives, the context is clear: it is for retrieving system status. No exclusion criteria are needed as siblings are distinct.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

mkp_trigger_conversationC

Trigger MKP system for conversation analysis

ParametersJSON Schema

Name	Required	Description	Default
`user_input`	Yes	User input to trigger MKP conversation analysis
`user_profile`	No	Optional user profile data as JSON string	{}

TDQS

C2.5/5.0

Behavior2/5

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 only says 'trigger', implying initiation, but does not disclose side effects, authorization needs, or whether it is destructive or read-only. This is insufficient for an agent to understand the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely short (one phrase) and lacks full sentences or structure. While it is concise, it sacrifices informativeness. It could be slightly more structured to improve readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return values, error states, or relationships with siblings. For a tool that triggers a system, more context is needed for proper use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond the schema: it does not explain how parameters relate to the tool's purpose or provide format/syntax details. The schema already describes both parameters adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Trigger MKP system for conversation analysis', which gives a general verb+resource but is vague and does not clearly distinguish from siblings like 'mkp_analyze_context' or 'mkp_enhance_cognition'. The purpose is implied but lacks specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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. No exclusions or context provided. The description merely states the action without any situational context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv1.0.0May 25, 2026
- First observedmkp_analyze_context
- First observedmkp_enhance_cognition
- First observedmkp_get_capabilities
- First observedmkp_get_system_status
- First observedmkp_trigger_conversation

Install Server

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TDQS

B3.4/5.0

Disambiguation5/5

Each tool targets a distinct aspect of the MKP system: analyzing context, enhancing cognition, listing capabilities, checking status, and triggering conversations. No two tools have overlapping purposes.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with the 'mkp_' prefix, making them predictable and easy to understand. The naming is uniform throughout.

Tool Count4/5

With 5 tools, the server is well-scoped for its specialized purpose. It covers the core interactions without being too sparse or overwhelming.

Completeness4/5

The tool set covers the main actions: getting info (capabilities, status), performing actions (analyze, enhance, trigger). Minor gaps like configuration or reset might exist, but core workflows are addressed.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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MKP MCP Server 🧠

🎯 What is MKP?

🚀 Why Use This MCP Server?

Before MKP

After MKP

🛠️ Installation

Prerequisites

Quick Install

⚙️ Configuration

Claude Code Integration

Alternative: Global Installation

🧰 Available Tools

1. mkp_trigger_conversation

2. mkp_get_system_status

3. mkp_get_capabilities

4. mkp_analyze_context

5. mkp_enhance_cognition

💡 Usage Patterns

1. Conversation Activation

2. Domain-Specific Enhancement

3. Context Analysis

4. System Monitoring

🏗️ Architecture

Key Components

🔬 How It Works

1. Conversation Analysis

2. Cognitive Enhancement

3. Enhanced Response Generation

🌟 Use Cases

🚀 Space Technology

💰 Financial Technology

🧬 Biotechnology

🏗️ Infrastructure Development

📊 Performance Metrics

🔧 Development

Local Development

Project Structure

Contributing

🔒 Security & Privacy

🐛 Troubleshooting

Common Issues

📈 Roadmap

Version 2.0

Version 3.0

🤝 Community

📄 License

🙏 Acknowledgments

📞 Support

Available Tools

Tool Schema Changelog

Maintenance

Resources

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Latest Blog Posts

MCP directory API

1. `mkp_trigger_conversation`

2. `mkp_get_system_status`

3. `mkp_get_capabilities`

4. `mkp_analyze_context`

5. `mkp_enhance_cognition`