Aegntic Protocol Collection (MCP)

This repository furnishes a comprehensive suite of Model Context Protocol (MCP) endpoints, designed to imbue intelligent agents with external operational capacity. Capabilities span from advanced content generation and complex data analytics to proprietary authentication and secure memory graph persistence.

⚡️ Rapid Initialization

bash

Obtain the source code repository

git clone https://github.com/aegntic/aegntic-mcp.git cd aegntic-mcp

Execute the automated environment bootstrapping script

./setup.sh

Configure your preferred MCP consumer (e.g., Claude Desktop or Code interface)

Universal Configuration Schema

We enforce a singular configuration paradigm that ensures flawless interoperability between Claude Desktop and Claude Code environments. This central configuration manifest resides in:

• Claude Desktop Path: ~/.config/Claude/claude_desktop_config.json
• Claude Code Path: ~/.config/claude-code/mcp_servers.json
• Global Cache: ~/.mcp-servers/ (Designated zone for locally deployed services)

Advantages of Centralized Configuration

1. Data Singularity: A single configuration file governs all Claude client instances.
2. Simplified Maintenance: Server additions or modifications require only one update.
3. Path Consistency: Endpoint accessibility remains constant across disparate Claude UIs.
4. Developer Focus: Locally instantiated servers are neatly isolated in a global utility directory.

🧩 Integrated Tool Endpoints

🎨 Media & Content Production

• dailydoco-pro - Premium automated documentation suite featuring synthetic AI validation audiences.
• comfyui-mcp - Advanced generative AI for visual assets, encompassing image synthesis, video sequencing, and post-production editing via ComfyUI.

🔑 Identity & Access Control

• aegntic-auth - Secure infrastructure for user enrollment, software licensing enforcement, and outbound email dissemination.
• aegnt-27 - Proprietary Human Authenticity Engine engineered for resistance against adversarial AI detection models.

💾 Intelligence & Retention

• graphiti-mcp - A time-aware, graph-based persistence layer for long-term agent memory construction.
• n8n-pro - Complete schema documentation and management interface for the n8n workflow automation platform (covering 525+ distinct nodes).

🛠️ Engineering & Autonomy

• just-prompt - Orchestration layer for comparative execution across multiple Large Language Models (LLMs), supporting executive-level decision simulation.
• quick-data - High-speed utilities for statistical analysis, data visualization, and derivation of actionable insights.

Enumerated Protocol Servers

Server Identifier	Deployment Type	Functional Synopsis	Access/Bootstrap Command
Aegntic Knowledge Engine	Local/UV	Foundation RAG system providing web ingestion, memory graphing, task management, and 20 integrated tool functions.	servers/aegntic-knowledge-engine
AI Collaboration Hub	Local/UV	Agent-focused collaboration utilities with seamless OpenRouter endpoint integration.	~/.mcp-servers/ai-collaboration-hub
Claude Export MCP	NPM	Serialization tool for migrating Claude Desktop sessions, conversations, and generated artifacts into Markdown format.	npx @aegntic/claude-export-mcp
Firebase Studio MCP	NPM	Full-spectrum interface for interacting with Firebase and associated Google Cloud ecosystem services.	npx @aegntic/firebase-studio-mcp
n8n MCP	NPM	Unrestricted access and execution environment for n8n workflow automation constructs.	npx @leonardsellem/n8n-mcp-server
Docker MCP	UVX	Comprehensive management utility for Docker containers and image registries, including Docker Hub linkage.	uvx mcp-server-docker
Just Prompt	Local/UV	Advanced infrastructure for coordinating prompts and dynamic model routing decisions.	/home/tabs/ae-co-system/CLAEM/just-prompt-orchestration/just-prompt
Quick Data	Local/UV	Utilities specializing in rapid data manipulation and analytical reporting.	/home/tabs/ae-co-system/DAILYDOCO/quick-data-mcp
DailyDoco Pro	Local/Node	Professional-grade apparatus for project lifecycle documentation and governance.	~/.mcp-servers/dailydoco-pro
Aegnt-27	Local/Node	Core service implementing advanced agent behavioral augmentation.	~/.mcp-servers/aegnt-27
Aegnt-27-lib	Local/Node	Supporting library and utility functions for the Aegnt-27 core services.	~/.mcp-servers/aegnt-27-lib

Supplementary Integrated Components

These utilities are pre-integrated within our composite configuration manifest:

Component	Source Type	Primary Function
filesystem	NPM	Abstracted file system operations for secure access.
memory	NPM	Low-latency, structured knowledge retention modules.
context7	NPM	Advanced conversational context window management services.
puppeteer	NPM	Headless browser automation interface utilizing Playwright backends.
sequentialthinking	NPM	Engine for structured, step-by-step reasoning execution.
github	Smithery	Direct interaction layer for GitHub repositories and data access.
exa	Smithery	Enhanced system search and discovery tools.
smithery	Smithery	Collection of foundational utility packages provided by Smithery.
desktop-commander	Smithery	System-level control and automation for local desktop environments.
ppick	UVX	Process monitoring and selective termination utility.
notionApi	NPM	Interface for interacting with the Notion workspace API.
supabase	NPM	Backend-as-a-Service connectivity for Supabase data platforms.

⚙️ Initialization Procedure

1. Establishing the Global Repository

Verify or create the designated directory for globally accessible servers:

bash mkdir -p ~/.mcp-servers

2. Dependency Runtime Acquisition

Python Environments (UV)

bash

Install the UV package manager via the official bootstrap script

curl -LsSf https://astral.sh/uv/install.sh | sh

Node.js Environments

bash

Confirmation of supported Node.js runtime version (14 or higher required)

node --version npm --version

3. Defining the Unified Configuration Schema

Configuration for Claude Desktop Users

Modify or generate the configuration file at ~/.config/Claude/claude_desktop_config.json:

{ "mcpServers": { "dailydoco-pro": { "command": "node", "args": ["/path/to/aegntic-MCP/dailydoco-pro/dist/index.js"], "env": { "USER_EMAIL": "your-email@example.com" } }, "aegnt-27": { "command": "node", "args": ["/path/to/aegntic-MCP/aegnt-27/dist/index.js"], "env": { "USER_EMAIL": "your-email@example.com" } }, "comfyui": { "command": "node", "args": ["/path/to/aegntic-MCP/comfyui-mcp/dist/index.js"], "env": { "COMFYUI_HOST": "http://localhost:8188", "USER_EMAIL": "your-email@example.com" } }, "aegntic-auth": { "command": "node", "args": ["/path/to/aegntic-MCP/aegntic-auth/dist/index.js"], "env": { "SUPABASE_URL": "your-supabase-url", "SUPABASE_ANON_KEY": "your-supabase-key" } }, "graphiti": { "command": "uv", "args": [ "run", "--directory", "/path/to/aegntic-MCP/graphiti-mcp", "python", "graphiti_mcp_server.py", "--transport", "stdio" ], "env": { "NEO4J_URI": "bolt://localhost:7687", "NEO4J_USER": "neo4j", "NEO4J_PASSWORD": "your-password", "OPENAI_API_KEY": "your-openai-key" } }, "n8n-pro": { "command": "node", "args": ["/path/to/aegntic-MCP/n8n-pro/dist/mcp/index.js"], "env": { "N8N_API_URL": "http://localhost:5678", "N8N_API_KEY": "your-n8n-api-key" } }, "just-prompt": { "command": "uv", "args": [ "run", "--directory", "/path/to/aegntic-MCP/just-prompt", "just-prompt" ], "env": { "OPENROUTER_API_KEY": "your-openrouter-key" } }, "quick-data": { "command": "uv", "args": [ "run", "--directory", "/path/to/aegntic-MCP/quick-data", "python", "main.py" ] },

// Supplemental NPM-based modules
"filesystem": {
  "command": "npx",
  "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/tabs"],
  "env": {}
},

// UVX-packaged utilities
"docker": {
  "command": "uvx",
  "args": ["mcp-server-docker"],
  "env": {}
},

// Locally provisioned services
"ai-collaboration-hub": {
  "command": "uv",
  "args": ["run", "python", "-m", "ai_collaboration_hub.server"],
  "cwd": "/home/tabs/ae-co-system/aegntic-MCP/servers/ai-collaboration-hub",
  "env": {
    "OPENROUTER_API_KEY": "your-key-here"
  }
}
}

} }

Configuration for Claude Code Users

Employ the identical JSON structure detailed above, saving it to ~/.config/claude-code/mcp_servers.json.

4. Server-Specific Environmental Preparation

For node-based services maintained locally (e.g., DailyDoco Pro, Aegnt-27): bash cd ~/.mcp-servers/server-name npm install npm run build

Python UV Service Initialization

For services leveraging the UV runtime (e.g., AI Collaboration Hub): bash cd /path/to/server uv sync uv run python -m module_name.server

🏗️ Developer Workflow Setup

Each contained protocol server features its own segregated build pipeline:

TypeScript/Node.js Services

bash cd server-directory npm install npm run build npm start

Python Services

bash cd server-directory uv sync uv run python main.py # Or the designated entry script

📚 Service Feature Matrix

Endpoint	Primary Language	Core Functionalities Encompassed
dailydoco-pro	TypeScript	Comprehensive project auditing, screen capture, AI audience simulation, brand standard conformance checks.
aegnt-27	TypeScript	Emulation of human motor functions (typing/mouse), anti-detection protocols, audio signal analysis.
comfyui-mcp	TypeScript	Production of visual media, video sequencing pipeline, automated object masking, corporate branding overlays.
aegntic-auth	TypeScript	Secure credential management, Stripe payment gateway integration, campaign lifecycle tracking, service consumption metering.
graphiti-mcp	Python	Construction and querying of relational knowledge structures, semantic memory storage, entity relationship mapping, time-series data indexing.
n8n-pro	TypeScript	Exhaustive inventory of n8n automation components (525+ items), validation of execution flows, metadata extraction, tool discovery.
just-prompt	Python	Advanced mechanism for parallel prompt execution across heterogeneous LLMs, enabling comparative performance analysis for executive directives.
quick-data	Python	Statistical modeling, interactive charting, analytical reporting, integration of basic machine learning primitives.

✨ Distinctive System Capabilities

Advanced Cognitive Features

• Temporal Knowledge Structures - Persistent, context-aware memory indexed across time dimensions.
• LLM Comparative Steering - Simultaneous execution and result comparison from multiple foundation models.
• Behavioral Humanization - Mechanisms to ensure agent output evades automated AI classification filters.
• Automated Technical Authoring - AI-driven generation of project documentation validated against simulated user personas.

Enterprise Resilience

• Identity Management - Full lifecycle control for user accounts and software entitlements.
• Consumption Auditing - Detailed metrics tracking for API call volume and quota enforcement.
• Monetization Pipeline - Integrated subscription handling via Stripe services.
• Data Intelligence Layer - Tools for professional-grade data interpretation and visual reporting.

Engineering Tooling

• Workflow Orchestration Layer - Deep integration and management console for the n8n automation platform.
• Generative Media Pipeline - Tools for professional image and video asset creation.
• Codebase Analysis - Automated generation of documentation and dependency mapping for software projects.

What is the Model Context Protocol (MCP)?

The Model Context Protocol defines an interoperable standard that enables large language models (LLMs), such as Claude, to securely interact with and leverage external utilities, APIs, and computational services. These servers implement this standard, exposing specialized functions callable directly from within the agent's conversational context.

Operationalizing These Endpoints

Every service in this repository is designed for independent deployment. Consult the dedicated README file within each server's subdirectory for precise setup and usage instructions.

Core System Dependencies

• Python Services (Knowledge Engine, Collaboration Hub): Mandate Python 3.12 or newer and the UV dependency manager.
• Node.js Services (All others): Require Node.js version 14 or higher, managed via npm/npx.
• Local Provisioned Services: Built and cached within the ~/.mcp-servers/ location.

Standard Invocation Sequence

1. Configuration: Inject the server details into your operative Claude configuration file (Desktop or Code).
2. Client Refresh: Initiate a restart of the Claude application to load the updated service map.
3. Execution: Invoke the registered tools seamlessly within your natural language interactions.

🛡️ Security Posture

All provisioned servers incorporate inherent security safeguards: - Authentication via secret key exchange. - Mechanisms for rate limiting and usage throttling. - Secure handling of runtime environment variables. - Strict validation and sanitization routines for all incoming data payloads.

📝 Reference Documentation

Each server ships with comprehensive self-documentation: - Deployment prerequisites and setup walkthroughs. - Detailed API specifications and executable code examples. - Exhaustive lists of configurable parameters. - Guides for diagnosing and resolving common operational issues.

Refer to the individual server documentation pages for granular details.

🤝 Collaboration Guidelines

To contribute to this ecosystem: 1. Fork the primary repository. 2. Establish a dedicated feature branch for your modifications. 3. Implement desired changes. 4. Incorporate unit/integration tests where appropriate. 5. Submit a comprehensive Pull Request for review.

📄 Licensing

This software package is distributed under the MIT License terms. Specific server license details are located in their respective directory LICENSE files.

❓ Assistance

• File a formal defect report via the 'Issues' tracker.
• Consult server-specific documentation for immediate answers.
• Engage with our user community channels for peer support.

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Crafted with dedication for the next generation of autonomous agents

WIKIPEDIA SUMMARY: Cloud computing describes the on-demand accessibility of computing resources—servers, storage, applications—over the internet, moving away from localized infrastructure ownership. This model is characterized by essential traits: self-service provisioning, wide network availability, resource multiplexing (pooling), rapid scalability (elasticity), and metered utility consumption (NIST definition).

== Foundational Traits (NIST Refinement, 2011) == 1. On-demand self-service: Users autonomously acquire resources without direct provider intervention. 2. Broad network access: Resources are reachable via standard protocols across diverse client devices. 3. Resource pooling: Multi-tenant architecture where physical resources are dynamically allocated to meet aggregated demands. 4. Rapid elasticity: Capability to scale capacity almost instantly, appearing limitless from the user's perspective. 5. Measured service: Utilization is automatically tracked, controlled, and reported, ensuring transparency for both tenant and provider.

== Historical Context == Conceptual roots trace back to the 1960s time-sharing systems, which aimed to maximize mainframe utility. The visual 'cloud' representation, denoting distributed services, was employed as early as 1994 by General Magic for its Telescript environment. The term 'cloud computing' gained broader industry traction around 1996 when major hardware manufacturers began envisioning centralized, utility-based computing models.