MCP Agentic AI Server
by itsDurvank
- Mcp_server
- documentation
designs.md•19.2 kB
# 🏗️ MCP Agentic AI Server - Architecture Designs
This document contains comprehensive Mermaid diagrams illustrating the system architecture, component designs, and technical infrastructure of the MCP Agentic AI Server project.
## 📋 Table of Contents
1. [High-Level System Architecture](#high-level-system-architecture)
2. [Component Architecture](#component-architecture)
3. [Data Architecture](#data-architecture)
4. [Network Architecture](#network-architecture)
5. [Security Architecture](#security-architecture)
6. [Deployment Architecture](#deployment-architecture)
7. [Microservices Architecture](#microservices-architecture)
8. [Database Design](#database-design)
9. [API Architecture](#api-architecture)
10. [Monitoring Architecture](#monitoring-architecture)
---
## 🌐 High-Level System Architecture
```mermaid
C4Context
title System Context Diagram - MCP Agentic AI Server
Person(user, "End User", "Interacts with AI agents through web interface")
System_Boundary(mcp_system, "MCP Agentic AI System") {
System(dashboard, "Streamlit Dashboard", "Interactive web interface for AI interactions")
System(custom_mcp, "Custom MCP Server", "Task-based AI processing with tool integration")
System(public_mcp, "Public MCP Server", "Direct AI query processing")
}
System_Ext(gemini, "Google Gemini API", "AI language model service")
System_Ext(browser, "Web Browser", "User interface client")
Rel(user, browser, "Uses")
Rel(browser, dashboard, "HTTP/WebSocket")
Rel(dashboard, custom_mcp, "REST API")
Rel(dashboard, public_mcp, "REST API")
Rel(custom_mcp, gemini, "HTTPS API")
Rel(public_mcp, gemini, "HTTPS API")
```
---
## 🔧 Component Architecture
```mermaid
graph TB
subgraph "🎨 Presentation Layer"
A[Streamlit Dashboard]
A1[Real-time Stats Display]
A2[Interactive Forms]
A3[Response Visualization]
A --> A1
A --> A2
A --> A3
end
subgraph "🔄 Application Layer"
B[Custom MCP Server]
C[Public MCP Server]
subgraph "🔧 Custom MCP Components"
B1[Flask Web Server]
B2[MCP Controller]
B3[Task Manager]
B4[Tool Framework]
B --> B1
B --> B2
B --> B3
B --> B4
end
subgraph "🌐 Public MCP Components"
C1[Flask Web Server]
C2[Direct AI Handler]
C3[Statistics Tracker]
C --> C1
C --> C2
C --> C3
end
end
subgraph "🛠️ Service Layer"
D[Gemini AI Client]
E[Configuration Manager]
F[Logging Service]
G[Statistics Engine]
end
subgraph "💾 Data Layer"
H[In-Memory Task Store]
I[Statistics Cache]
J[Configuration Files]
K[Log Files]
end
A1 --> G
A2 --> B1
A2 --> C1
B2 --> D
B3 --> H
B4 --> D
C2 --> D
C3 --> I
B2 --> G
C2 --> G
D --> E
G --> F
F --> K
E --> J
style A fill:#e1f5fe
style B fill:#e8f5e8
style C fill:#fff3e0
style D fill:#ffebee
```
---
## 📊 Data Architecture
```mermaid
erDiagram
TASK {
string task_id PK
string input
array tools
timestamp created_at
string status
}
STATISTICS {
int queries_processed
float total_response_time
int successful_queries
int failed_queries
timestamp session_start_time
int active_sessions
}
TOOL_EXECUTION {
string execution_id PK
string task_id FK
string tool_name
string input_data
string output_data
float execution_time
string status
}
AI_RESPONSE {
string response_id PK
string task_id FK
string prompt
string response
float response_time
string model_used
timestamp created_at
}
USER_SESSION {
string session_id PK
timestamp start_time
timestamp last_activity
int query_count
string user_agent
}
TASK ||--o{ TOOL_EXECUTION : "has"
TASK ||--|| AI_RESPONSE : "generates"
USER_SESSION ||--o{ TASK : "creates"
STATISTICS ||--o{ TASK : "tracks"
```
---
## 🌐 Network Architecture
```mermaid
graph TB
subgraph "🌍 Internet"
U[👤 Users]
end
subgraph "🔒 DMZ Zone"
LB[⚖️ Load Balancer<br/>nginx/HAProxy]
FW[🛡️ Firewall<br/>iptables/UFW]
end
subgraph "🏢 Application Zone"
subgraph "🎨 Frontend Tier"
ST[🎨 Streamlit<br/>Port 8501]
end
subgraph "⚙️ Backend Tier"
CM[🔧 Custom MCP<br/>Port 8000]
PM[🌐 Public MCP<br/>Port 8001]
end
subgraph "📊 Monitoring Tier"
MON[📊 Monitoring<br/>Prometheus/Grafana]
LOG[📝 Logging<br/>ELK Stack]
end
end
subgraph "🌐 External Services"
GM[🧠 Google Gemini API<br/>HTTPS/443]
end
U --> FW
FW --> LB
LB --> ST
ST --> CM
ST --> PM
CM --> GM
PM --> GM
CM --> MON
PM --> MON
ST --> MON
CM --> LOG
PM --> LOG
ST --> LOG
style U fill:#e1f5fe
style LB fill:#fff3e0
style FW fill:#ffebee
style GM fill:#f3e5f5
```
---
## 🔐 Security Architecture
```mermaid
graph TB
subgraph "🛡️ Security Layers"
subgraph "🌐 Network Security"
A[🔥 Firewall Rules]
B[🔒 HTTPS/TLS]
C[🚫 Rate Limiting]
end
subgraph "🔑 Authentication & Authorization"
D[🔐 API Key Management]
E[🎫 Session Management]
F[👤 User Validation]
end
subgraph "📊 Data Security"
G[🔒 Input Sanitization]
H[🛡️ Output Filtering]
I[📝 Audit Logging]
end
subgraph "🏗️ Application Security"
J[⚠️ Error Handling]
K[🔍 Input Validation]
L[🚨 Security Headers]
end
end
subgraph "🎯 Attack Vectors & Mitigations"
M[🚫 SQL Injection → Input Validation]
N[🚫 XSS → Output Encoding]
O[🚫 CSRF → Token Validation]
P[🚫 DDoS → Rate Limiting]
Q[🚫 Data Breach → Encryption]
end
A --> D
B --> E
C --> F
D --> G
E --> H
F --> I
G --> J
H --> K
I --> L
style A fill:#ffebee
style D fill:#fff3e0
style G fill:#e8f5e8
style J fill:#e1f5fe
```
---
## 🚀 Deployment Architecture
```mermaid
graph TB
subgraph "☁️ Cloud Infrastructure"
subgraph "🌐 Production Environment"
subgraph "🔄 Load Balancer Tier"
LB1[⚖️ Primary LB]
LB2[⚖️ Secondary LB]
end
subgraph "🎨 Frontend Tier"
ST1[🎨 Streamlit Instance 1]
ST2[🎨 Streamlit Instance 2]
ST3[🎨 Streamlit Instance 3]
end
subgraph "⚙️ Backend Tier"
CM1[🔧 Custom MCP 1]
CM2[🔧 Custom MCP 2]
PM1[🌐 Public MCP 1]
PM2[🌐 Public MCP 2]
end
subgraph "💾 Data Tier"
RD[📊 Redis Cache]
DB[🗄️ PostgreSQL]
FS[📁 File Storage]
end
end
subgraph "🧪 Staging Environment"
ST_STG[🎨 Streamlit Staging]
CM_STG[🔧 Custom MCP Staging]
PM_STG[🌐 Public MCP Staging]
end
subgraph "🔧 Development Environment"
ST_DEV[🎨 Streamlit Dev]
CM_DEV[🔧 Custom MCP Dev]
PM_DEV[🌐 Public MCP Dev]
end
end
subgraph "🔄 CI/CD Pipeline"
GIT[📚 Git Repository]
BUILD[🔨 Build Process]
TEST[🧪 Automated Tests]
DEPLOY[🚀 Deployment]
end
LB1 --> ST1
LB1 --> ST2
LB2 --> ST3
ST1 --> CM1
ST2 --> CM2
ST3 --> PM1
ST1 --> PM2
CM1 --> RD
CM2 --> RD
PM1 --> DB
PM2 --> DB
GIT --> BUILD
BUILD --> TEST
TEST --> DEPLOY
DEPLOY --> ST_STG
DEPLOY --> CM_STG
DEPLOY --> PM_STG
style LB1 fill:#fff3e0
style ST1 fill:#e1f5fe
style CM1 fill:#e8f5e8
style RD fill:#f3e5f5
```
---
## 🔄 Microservices Architecture
```mermaid
graph TB
subgraph "🎯 API Gateway"
GW[🚪 API Gateway<br/>Kong/Zuul]
end
subgraph "🔧 Core Services"
subgraph "🎨 UI Service"
UI[🎨 Streamlit Service<br/>Port 8501]
end
subgraph "🤖 AI Services"
CM[🔧 Custom MCP Service<br/>Port 8000]
PM[🌐 Public MCP Service<br/>Port 8001]
end
subgraph "🛠️ Support Services"
TS[📋 Task Service]
SS[📊 Statistics Service]
LS[📝 Logging Service]
CS[⚙️ Config Service]
end
end
subgraph "💾 Data Services"
CACHE[📊 Redis Cache]
DB[🗄️ Database]
QUEUE[📬 Message Queue]
end
subgraph "🌐 External Services"
GEMINI[🧠 Gemini API]
MONITOR[📊 Monitoring]
end
GW --> UI
GW --> CM
GW --> PM
UI --> CM
UI --> PM
UI --> SS
CM --> TS
CM --> SS
CM --> GEMINI
PM --> SS
PM --> GEMINI
TS --> CACHE
SS --> CACHE
LS --> DB
CS --> DB
CM --> QUEUE
PM --> QUEUE
SS --> MONITOR
LS --> MONITOR
style GW fill:#fff3e0
style UI fill:#e1f5fe
style CM fill:#e8f5e8
style GEMINI fill:#ffebee
```
---
## 🗄️ Database Design
```mermaid
graph TB
subgraph "💾 Data Storage Architecture"
subgraph "🔄 Operational Data"
CACHE[📊 Redis Cache<br/>• Session Data<br/>• Statistics<br/>• Temporary Results]
MEMORY[💭 In-Memory Store<br/>• Active Tasks<br/>• Real-time Metrics<br/>• User Sessions]
end
subgraph "📚 Persistent Data"
LOGS[📝 Log Files<br/>• Application Logs<br/>• Error Logs<br/>• Access Logs]
CONFIG[⚙️ Configuration<br/>• YAML Files<br/>• Environment Variables<br/>• API Keys]
end
subgraph "📊 Analytics Data"
METRICS[📈 Time Series DB<br/>• Performance Metrics<br/>• Usage Statistics<br/>• Historical Data]
AUDIT[🔍 Audit Trail<br/>• User Actions<br/>• System Events<br/>• Security Logs]
end
end
subgraph "🔄 Data Flow"
APP[🎯 Application Layer]
APP --> CACHE
APP --> MEMORY
APP --> LOGS
APP --> CONFIG
CACHE --> METRICS
MEMORY --> METRICS
LOGS --> AUDIT
end
style CACHE fill:#e1f5fe
style MEMORY fill:#fff3e0
style LOGS fill:#e8f5e8
style METRICS fill:#f3e5f5
```
---
## 🔌 API Architecture
```mermaid
graph TB
subgraph "🌐 API Layer Architecture"
subgraph "🚪 API Gateway"
GW[🚪 Gateway Router<br/>• Authentication<br/>• Rate Limiting<br/>• Load Balancing]
end
subgraph "🔧 Custom MCP API"
CM_API[🔧 Custom MCP Endpoints]
CM_TASK[📋 POST /task<br/>Create Task]
CM_RUN[▶️ POST /task/{id}/run<br/>Execute Task]
CM_STATS[📊 GET /stats<br/>Get Statistics]
CM_API --> CM_TASK
CM_API --> CM_RUN
CM_API --> CM_STATS
end
subgraph "🌐 Public MCP API"
PM_API[🌐 Public MCP Endpoints]
PM_ASK[💬 POST /ask<br/>Direct Query]
PM_STATS[📊 GET /stats<br/>Get Statistics]
PM_API --> PM_ASK
PM_API --> PM_STATS
end
subgraph "🎨 Dashboard API"
UI_API[🎨 Streamlit Interface]
UI_FORM[📝 Form Handlers]
UI_DISPLAY[📊 Data Display]
UI_API --> UI_FORM
UI_API --> UI_DISPLAY
end
end
subgraph "📊 API Documentation"
SWAGGER[📚 OpenAPI/Swagger<br/>• Endpoint Documentation<br/>• Request/Response Schemas<br/>• Interactive Testing]
end
subgraph "🔍 API Monitoring"
MONITOR[📊 API Analytics<br/>• Response Times<br/>• Error Rates<br/>• Usage Patterns]
end
GW --> CM_API
GW --> PM_API
GW --> UI_API
CM_API --> SWAGGER
PM_API --> SWAGGER
CM_API --> MONITOR
PM_API --> MONITOR
UI_API --> MONITOR
style GW fill:#fff3e0
style CM_API fill:#e8f5e8
style PM_API fill:#e1f5fe
style SWAGGER fill:#f3e5f5
```
---
## 📊 Monitoring Architecture
```mermaid
graph TB
subgraph "📊 Monitoring Stack"
subgraph "📈 Metrics Collection"
PROM[📊 Prometheus<br/>• System Metrics<br/>• Application Metrics<br/>• Custom Metrics]
NODE[🖥️ Node Exporter<br/>• CPU, Memory<br/>• Disk, Network<br/>• System Health]
APP_METRICS[📱 Application Metrics<br/>• Response Times<br/>• Error Rates<br/>• Business Metrics]
end
subgraph "📊 Visualization"
GRAFANA[📊 Grafana<br/>• Dashboards<br/>• Alerts<br/>• Reports]
DASH[📈 Custom Dashboards<br/>• Real-time Stats<br/>• Performance Trends<br/>• Usage Analytics]
end
subgraph "🚨 Alerting"
ALERT[🚨 Alert Manager<br/>• Threshold Alerts<br/>• Escalation Rules<br/>• Notification Routing]
NOTIFY[📧 Notifications<br/>• Email Alerts<br/>• Slack Integration<br/>• SMS Alerts]
end
subgraph "📝 Logging"
ELK[📝 ELK Stack<br/>• Elasticsearch<br/>• Logstash<br/>• Kibana]
LOGS[📋 Log Aggregation<br/>• Application Logs<br/>• Error Logs<br/>• Access Logs]
end
end
subgraph "🎯 Monitored Services"
ST[🎨 Streamlit]
CM[🔧 Custom MCP]
PM[🌐 Public MCP]
SYS[🖥️ System Resources]
end
ST --> APP_METRICS
CM --> APP_METRICS
PM --> APP_METRICS
SYS --> NODE
APP_METRICS --> PROM
NODE --> PROM
PROM --> GRAFANA
PROM --> ALERT
GRAFANA --> DASH
ALERT --> NOTIFY
ST --> LOGS
CM --> LOGS
PM --> LOGS
LOGS --> ELK
style PROM fill:#e1f5fe
style GRAFANA fill:#e8f5e8
style ALERT fill:#fff3e0
style ELK fill:#f3e5f5
```
---
## 🔄 Event-Driven Architecture
```mermaid
graph TB
subgraph "📬 Event Bus"
EB[📬 Message Queue<br/>Redis Pub/Sub]
end
subgraph "📤 Event Publishers"
CM_PUB[🔧 Custom MCP<br/>• Task Created<br/>• Task Completed<br/>• Tool Executed]
PM_PUB[🌐 Public MCP<br/>• Query Received<br/>• Response Generated<br/>• Error Occurred]
UI_PUB[🎨 Dashboard<br/>• User Action<br/>• Page View<br/>• Form Submit]
end
subgraph "📥 Event Subscribers"
STATS_SUB[📊 Statistics Service<br/>• Update Metrics<br/>• Calculate Rates<br/>• Store History]
LOG_SUB[📝 Logging Service<br/>• Write Logs<br/>• Index Events<br/>• Archive Data]
MONITOR_SUB[📊 Monitoring Service<br/>• Track Performance<br/>• Generate Alerts<br/>• Update Dashboards]
NOTIFY_SUB[📧 Notification Service<br/>• Send Alerts<br/>• User Notifications<br/>• System Messages]
end
CM_PUB --> EB
PM_PUB --> EB
UI_PUB --> EB
EB --> STATS_SUB
EB --> LOG_SUB
EB --> MONITOR_SUB
EB --> NOTIFY_SUB
style EB fill:#fff3e0
style CM_PUB fill:#e8f5e8
style STATS_SUB fill:#e1f5fe
```
---
## 🏗️ Infrastructure as Code
```mermaid
graph TB
subgraph "🔧 Infrastructure Management"
subgraph "📋 Configuration Management"
TERRAFORM[🏗️ Terraform<br/>• Infrastructure Provisioning<br/>• Resource Management<br/>• State Management]
ANSIBLE[⚙️ Ansible<br/>• Configuration Management<br/>• Application Deployment<br/>• Service Orchestration]
end
subgraph "🐳 Containerization"
DOCKER[🐳 Docker<br/>• Application Containers<br/>• Multi-stage Builds<br/>• Image Management]
K8S[☸️ Kubernetes<br/>• Container Orchestration<br/>• Service Discovery<br/>• Auto-scaling]
end
subgraph "🔄 CI/CD Pipeline"
GITHUB[📚 GitHub Actions<br/>• Source Control<br/>• Automated Testing<br/>• Deployment Pipeline]
JENKINS[🔄 Jenkins<br/>• Build Automation<br/>• Testing Pipeline<br/>• Deployment Orchestration]
end
end
subgraph "☁️ Cloud Providers"
AWS[☁️ AWS<br/>• EC2 Instances<br/>• Load Balancers<br/>• RDS Database]
GCP[☁️ Google Cloud<br/>• Compute Engine<br/>• Cloud SQL<br/>• Cloud Storage]
AZURE[☁️ Azure<br/>• Virtual Machines<br/>• App Services<br/>• Cosmos DB]
end
TERRAFORM --> AWS
TERRAFORM --> GCP
TERRAFORM --> AZURE
ANSIBLE --> DOCKER
DOCKER --> K8S
GITHUB --> JENKINS
JENKINS --> K8S
style TERRAFORM fill:#e1f5fe
style DOCKER fill:#e8f5e8
style K8S fill:#fff3e0
style AWS fill:#f3e5f5
```
---
## 🎯 Summary
These architecture diagrams provide a comprehensive view of the MCP Agentic AI Server system design:
### 🏗️ **Key Architectural Patterns**
- **Microservices Architecture**: Loosely coupled, independently deployable services
- **Event-Driven Design**: Asynchronous communication through message queues
- **Layered Architecture**: Clear separation of concerns across presentation, application, and data layers
- **API-First Design**: Well-defined interfaces for all service interactions
### 🔧 **Technology Stack**
- **Frontend**: Streamlit with modern CSS and responsive design
- **Backend**: Flask-based microservices with Python
- **AI Integration**: Google Gemini API with robust error handling
- **Data Storage**: Redis for caching, in-memory for real-time data
- **Monitoring**: Prometheus, Grafana, and ELK stack
- **Deployment**: Docker containers with Kubernetes orchestration
### 📊 **Scalability Features**
- **Horizontal Scaling**: Multiple instances of each service
- **Load Balancing**: Traffic distribution across service instances
- **Caching Strategy**: Redis for performance optimization
- **Monitoring**: Comprehensive observability and alerting
### 🔐 **Security Considerations**
- **API Security**: Authentication, authorization, and rate limiting
- **Data Protection**: Input validation, output sanitization, and encryption
- **Network Security**: Firewall rules, HTTPS/TLS, and secure communication
- **Audit Trail**: Comprehensive logging and monitoring for security events
These designs serve as blueprints for implementing, scaling, and maintaining the MCP Agentic AI Server system in production environments.
Latest Blog Posts
- MCP Moves to the Linux Foundation: Neutral Stewardship for Agentic InfrastructureBy Om-Shree-0709 on .mcpanthropicLinux Foundation
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/itsDurvank/Mcp_server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server