Skip to content

📄 Official and Strategic MCP Servers for ConnectSoft AI Software Factory

🎯 Purpose of This Document

This document provides a structured list of official, recognized, and strategically important Model Context Protocol (MCP) servers selected for integration into the ConnectSoft AI Software Factory.

Each MCP server is evaluated for its: - Alignment to ConnectSoft platform principles: Cloud-native, Event-driven, Multi-tenant SaaS, Observability-first. - Practical use for agent memory, artifact management, event streaming, secrets management, browser automation, and more. - Readiness for enterprise adoption, governance, and scalability.

📋 List of MCP Servers

# MCP Server Description Strategic Use Cases for ConnectSoft
1 Azure MCP Server Microsoft official MCP server exposing Azure resources (Blob, Event Grid, Key Vault, CosmosDB, Service Bus). Primary memory, artifact storage, events, secret management, multi-region resilience.
2 PostgreSQL MCP Server Exposes PostgreSQL relational databases as MCP structured memory. Structured semantic memory: vision documents, project artifacts, traceability.
3 MinIO MCP Server S3-compatible object storage MCP server (official). Private SaaS tenant artifact storage, on-premise and cloud-native deployments.
4 FileSystem MCP Server Mounts local or network file systems as MCP addressable contexts. Dev/test local memory, offline artifacts sandbox, hybrid agent development.
5 Azure DevOps MCP Server MCP exposure for Azure DevOps pipelines, repositories, artifacts, work items. GitOps integration, DevOps pipeline automation, work item memory.
6 GitHub MCP Server MCP interface for GitHub repositories, issues, pull requests. Public ConnectSoft SaaS templates, open-source collaboration, external project memory.
7 Vault MCP Server HashiCorp Vault secrets exposed as MCP memory. Cross-cloud secret management, credential security for multi-cloud SaaS.
8 Redis MCP Server In-memory MCP server exposing Redis for fast ephemeral memory. Agent session management, distributed ephemeral context passing.
9 ElasticSearch MCP Server Exposes ElasticSearch indices as MCP searchable contexts. Semantic full-text search over vision documents, architecture artifacts, observability logs.
10 Kafka MCP Server Event-driven streaming MCP server over Kafka topics. Agent event orchestration, workflow resilience, retries.
11 MongoDB MCP Server Exposes MongoDB document collections via MCP protocol. Flexible unstructured document memory, rapid prototyping memory layers.
12 Azure Service Bus MCP Server MCP access to Azure Service Bus queues and topics. High-reliability agent event queueing, cross-agent communication.
13 Azure Blob Storage Dedicated MCP Server Specialized MCP server for Azure Blob Storage. Direct blob storage for large artifacts, versioned snapshots, backups.
14 Azure Key Vault Dedicated MCP Server Specialized MCP server for Azure Key Vault secrets, keys, and certificates. Secrets injection, certificate management, secure configuration at runtime.
15 Snowflake MCP Server BigData enterprise warehouse exposed via MCP standard. AI training datasets, structured analytics memory for agents.
16 CosmosDB MCP Server Global distributed document store (Azure CosmosDB) through MCP. Replicated artifact memory, multi-region ConnectSoft SaaS support.
17 Google Cloud Storage MCP Server GCP Storage buckets MCP integration. Multi-cloud SaaS deployment support (GCP + Azure interoperability).
18 Amazon S3 MCP Server AWS S3 buckets MCP integration. Hybrid cloud SaaS storage options, customer-specific deployments.
19 Azure Tables MCP Server Azure Table Storage MCP mapping for key-value metadata. Lightweight metadata memory, microservice state stores.
20 TimescaleDB MCP Server PostgreSQL-based time-series database exposed via MCP. Observability telemetry storage (metrics, health checks, events timelines).
21 Puppeteer MCP Server Browser session management and navigation via Puppeteer and MCP. Browser-based automated QA testing, synthetic monitoring, web scraping.
22 Playwright MCP Server Cross-browser automation MCP server (Chrome, Firefox, Safari) using Playwright. Full E2E SaaS validation across browsers, compatibility testing, enriched data extraction.

📈 Strategic Categorization

Category MCP Servers
Primary Artifact and Memory Storage Azure MCP Server, PostgreSQL MCP Server, MinIO MCP Server, MongoDB MCP Server
Event Streaming and Asynchronous Orchestration Kafka MCP Server, Azure Service Bus MCP Server
Secrets Management Azure Key Vault MCP Server, Vault MCP Server
Fast Ephemeral Memory Redis MCP Server
Search and Observability ElasticSearch MCP Server, TimescaleDB MCP Server
DevOps and GitOps Integration Azure DevOps MCP Server, GitHub MCP Server
Hybrid and Multi-Cloud Deployment Support GCP Storage MCP Server, Amazon S3 MCP Server, MinIO MCP Server
Browser Automation and Web Interaction Puppeteer MCP Server, Playwright MCP Server

Integration Patterns

Pattern 1: Multi-Layer Memory Architecture

Use Case: Agents need both fast ephemeral memory and persistent artifact storage.

Pattern:

graph TB
    Agent[AI Agent] --> Redis[Redis MCP Server<br/>Ephemeral Session Memory]
    Agent --> PostgreSQL[PostgreSQL MCP Server<br/>Structured Artifacts]
    Agent --> AzureBlob[Azure Blob MCP Server<br/>Large Artifacts]

    Redis --> Session[Session State<br/>Context Cache]
    PostgreSQL --> Artifacts[Vision Docs<br/>Blueprints<br/>Traces]
    AzureBlob --> Files[Generated Code<br/>Binaries<br/>Snapshots]

    style Agent fill:#e3f2fd
    style Redis fill:#fff3e0
    style PostgreSQL fill:#e8f5e9
    style AzureBlob fill:#f3e5f5
Hold "Alt" / "Option" to enable pan & zoom

Implementation:

  1. Redis MCP Server - Fast access to current session context, agent state
  2. PostgreSQL MCP Server - Structured storage for vision documents, blueprints, traceability
  3. Azure Blob MCP Server - Large artifacts, generated code snapshots, binaries

Benefits:

  • Fast access to frequently used data (Redis)
  • Structured querying for artifacts (PostgreSQL)
  • Cost-effective storage for large files (Azure Blob)

Pattern 2: Event-Driven Agent Orchestration

Use Case: Coordinating multiple agents through event-driven workflows.

Pattern:

graph LR
    VisionAgent[Vision Architect Agent] --> ServiceBus[Azure Service Bus MCP<br/>Event Queue]
    ServiceBus --> ArchitectAgent[Solution Architect Agent]
    ArchitectAgent --> ServiceBus
    ServiceBus --> DeveloperAgent[Backend Developer Agent]
    DeveloperAgent --> ServiceBus
    ServiceBus --> DevOpsAgent[DevOps Engineer Agent]

    style VisionAgent fill:#e3f2fd
    style ServiceBus fill:#f3e5f5
    style ArchitectAgent fill:#e8f5e9
    style DeveloperAgent fill:#fff3e0
    style DevOpsAgent fill:#ffebee
Hold "Alt" / "Option" to enable pan & zoom

Implementation:

  1. Azure Service Bus MCP Server - Reliable event queueing for agent communication
  2. Kafka MCP Server - Alternative for high-throughput event streaming
  3. Redis MCP Server - Fast event routing and state management

Benefits:

  • Decoupled agent communication
  • Resilience through queueing
  • Scalable event processing

Pattern 3: Secrets Management for Multi-Tenant SaaS

Use Case: Secure secret management across multiple tenants and environments.

Pattern:

graph TB
    Agent[AI Agent] --> KeyVault[Azure Key Vault MCP<br/>Primary Secrets]
    Agent --> Vault[HashiCorp Vault MCP<br/>Cross-Cloud Secrets]

    KeyVault --> AzureSecrets[Azure Resources<br/>Connection Strings<br/>API Keys]
    Vault --> MultiCloud[Multi-Cloud Secrets<br/>GCP/AWS Credentials<br/>Cross-Platform]

    style Agent fill:#e3f2fd
    style KeyVault fill:#f3e5f5
    style Vault fill:#e8f5e9
Hold "Alt" / "Option" to enable pan & zoom

Implementation:

  1. Azure Key Vault MCP Server - Primary secrets for Azure resources
  2. Vault MCP Server - Cross-cloud and on-premise secret management

Benefits:

  • Centralized secret management
  • Secure multi-tenant isolation
  • Cross-cloud compatibility

Pattern 4: Observability and Search Integration

Use Case: Agents need to search and analyze vision documents, architecture artifacts, and observability data.

Pattern:

graph TB
    Agent[AI Agent] --> ElasticSearch[ElasticSearch MCP<br/>Full-Text Search]
    Agent --> TimescaleDB[TimescaleDB MCP<br/>Time-Series Data]

    ElasticSearch --> Docs[Vision Documents<br/>Architecture Artifacts<br/>Code Patterns]
    TimescaleDB --> Metrics[Metrics<br/>Health Checks<br/>Event Timelines]

    style Agent fill:#e3f2fd
    style ElasticSearch fill:#fff3e0
    style TimescaleDB fill:#e8f5e9
Hold "Alt" / "Option" to enable pan & zoom

Implementation:

  1. ElasticSearch MCP Server - Semantic search over documentation and artifacts
  2. TimescaleDB MCP Server - Time-series data for observability and metrics

Benefits:

  • Fast semantic search
  • Time-series analysis
  • Comprehensive observability

Pattern 5: DevOps and GitOps Integration

Use Case: Agents need to interact with source control, work items, and CI/CD pipelines.

Pattern:

graph LR
    Agent[AI Agent] --> AzureDevOps[Azure DevOps MCP<br/>Primary DevOps]
    Agent --> GitHub[GitHub MCP<br/>Public Repos]

    AzureDevOps --> Repos[Repositories<br/>Work Items<br/>Pipelines]
    GitHub --> PublicRepos[Public Templates<br/>Open Source<br/>External Projects]

    style Agent fill:#e3f2fd
    style AzureDevOps fill:#f3e5f5
    style GitHub fill:#e8f5e9
Hold "Alt" / "Option" to enable pan & zoom

Implementation:

  1. Azure DevOps MCP Server - Primary DevOps integration for internal projects
  2. GitHub MCP Server - Public repositories and open-source collaboration

Benefits:

  • Complete GitOps workflow
  • Automated CI/CD integration
  • Public and private repository support

Usage Examples

Example 1: Storing Vision Document in PostgreSQL

Scenario: Vision Architect Agent needs to store a vision document for later retrieval.

MCP Server: PostgreSQL MCP Server

Example Flow:

  1. Agent creates vision document
  2. Agent uses PostgreSQL MCP Server to store document
  3. Document stored with metadata (projectId, version, traceId)
  4. Other agents can query and retrieve document

Benefits:

  • Structured storage with query capabilities
  • Versioning and traceability
  • Fast retrieval for agent collaboration

Example 2: Retrieving Secrets for Deployment

Scenario: DevOps Engineer Agent needs database connection string for deployment.

MCP Server: Azure Key Vault MCP Server

Example Flow:

  1. Agent requests secret from Key Vault MCP Server
  2. Secret retrieved securely (tenant-isolated)
  3. Secret injected into deployment configuration
  4. Secret never exposed in logs or traces

Benefits:

  • Secure secret management
  • Tenant isolation
  • Audit trail for secret access

Example 3: Event-Driven Agent Handoff

Scenario: Vision Architect Agent completes vision document and hands off to Solution Architect Agent.

MCP Server: Azure Service Bus MCP Server

Example Flow:

  1. Vision Architect Agent publishes "VisionComplete" event
  2. Event queued in Azure Service Bus
  3. Solution Architect Agent receives event
  4. Solution Architect Agent retrieves vision document and begins work

Benefits:

  • Decoupled agent communication
  • Reliable event delivery
  • Scalable workflow orchestration

Example 4: Searching Architecture Patterns

Scenario: Solution Architect Agent needs to find similar architecture patterns from previous projects.

MCP Server: ElasticSearch MCP Server

Example Flow:

  1. Agent queries ElasticSearch for similar patterns
  2. Semantic search returns relevant architecture documents
  3. Agent uses patterns to inform current design
  4. New pattern stored for future reuse

Benefits:

  • Pattern reuse and consistency
  • Faster architecture design
  • Knowledge accumulation

Example 5: Browser-Based QA Testing

Scenario: QA Engineer Agent needs to perform end-to-end testing of generated web application.

MCP Server: Playwright MCP Server

Example Flow:

  1. Agent uses Playwright MCP Server to navigate to application
  2. Agent performs automated test scenarios
  3. Agent captures screenshots and logs
  4. Test results stored for analysis

Benefits:

  • Automated browser testing
  • Cross-browser compatibility validation
  • Visual regression testing

Best Practices

1. MCP Server Selection

Guidelines:

  • Primary Storage: Use Azure Blob MCP Server for large artifacts, PostgreSQL MCP Server for structured data
  • Fast Access: Use Redis MCP Server for session state and frequently accessed data
  • Secrets: Use Azure Key Vault MCP Server for Azure resources, Vault MCP Server for cross-cloud
  • Events: Use Azure Service Bus MCP Server for reliable queueing, Kafka MCP Server for high throughput
  • Search: Use ElasticSearch MCP Server for semantic search, TimescaleDB MCP Server for time-series

2. Security and Isolation

Guidelines:

  • Always use tenant-isolated MCP servers for multi-tenant scenarios
  • Never log or expose secrets retrieved from Key Vault or Vault MCP Servers
  • Use separate MCP server instances per tenant when required
  • Implement proper authentication and authorization for MCP server access

3. Performance Optimization

Guidelines:

  • Use Redis MCP Server for hot data, PostgreSQL for cold data
  • Implement caching strategies for frequently accessed data
  • Use appropriate storage tier (hot/cool/archive) for Azure Blob Storage
  • Monitor MCP server performance and optimize queries

4. Error Handling and Resilience

Guidelines:

  • Implement retry logic for transient MCP server failures
  • Use event queues (Service Bus, Kafka) for reliable agent communication
  • Implement circuit breakers for external MCP server dependencies
  • Log all MCP server interactions for debugging and audit

5. Observability

Guidelines:

  • Instrument all MCP server calls with tracing
  • Monitor MCP server performance metrics
  • Log MCP server errors and failures
  • Track MCP server usage patterns for optimization

Integration Checklist

When integrating a new MCP server:

  • Evaluate alignment with ConnectSoft platform principles
  • Assess security and compliance requirements
  • Test performance and scalability
  • Implement proper error handling
  • Add observability and monitoring
  • Document usage patterns and examples
  • Create integration tests
  • Update agent documentation

References