Which integrations are available for this server?

Provides AI capabilities using Google Gemini models to power agent responses for tasks such as strategy, architecture, and marketing plan generation. Provides AI capabilities using OpenAI's models (e.g., GPT-4) to power agent responses for business strategy, technology, design, sales, marketing, and quality assurance tasks.

How do I use Multi-agent MCP Network?

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., "@Multi-agent MCP Network Help me plan my startup's tech stack" 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.

Multi-agent MCP Network

by abdulsammad-lgtm

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Multi-Agent MCP Network

A production-ready Model Context Protocol (MCP) server that exposes multiple specialized AI agents through a unified interface. Each agent is an expert in its domain and they collaborate through an intelligent orchestrator.

Architecture

                    ┌─────────────────────┐
                    │   Client / MCP Host  │
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
                    │  REST API (Express)  │
                    │  MCP Server (SDK)   │
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
                    │    Orchestrator     │
                    │  ┌─────┴─────┐     │
                    │  │ Router    │     │
                    │  │ Workflow  │     │
                    │  └─────┬─────┘     │
                    └──────────┬──────────┘
                               │
          ┌────────────────────┼────────────────────┐
          ▼        ▼           ▼          ▼         ▼
       ┌────┐  ┌────┐     ┌────┐     ┌────┐    ┌────┐
       │CEO │  │CTO │ ... │Mktg│     │ QA │    │ ...│
       └────┘  └────┘     └────┘     └────┘    └────┘
          │        │          │          │
          └────────┴──────────┴──────────┘
                               │
                    ┌──────────▼──────────┐
                    │  Memory Layer       │
                    │  PostgreSQL + Qdrant│
                    └─────────────────────┘

Related MCP server: MCP Business AI Transformation

Agents

Agent	Role	Expertise
CEO	Business Strategy	Market analysis, product roadmap, pricing, investment, startup advice
CTO	Technology	Software architecture, backend, frontend, databases, AI/ML, DevOps, cloud
Designer	Design	UI/UX, brand identity, logos, color palettes, design systems
Sales	Revenue	Lead generation, cold emails, CRM, sales funnels, outreach
Marketing	Growth	SEO, content, ads, social media, email campaigns, competitor analysis
QA	Quality	Code review, bug finding, testing, security, performance

Tech Stack

Runtime: Node.js 20+, TypeScript
Framework: Express, FastMCP SDK (@modelcontextprotocol/sdk)
Databases: PostgreSQL (pgvector), Qdrant (vector), Redis (cache)
AI: OpenAI, Anthropic Claude, Google Gemini
Infrastructure: Docker, Docker Compose

Quick Start

Prerequisites

Node.js 20+
Docker & Docker Compose
At least one AI provider API key (OpenAI, Anthropic, or Gemini)

1. Clone & Install

cd multi-agent-mcp
npm install

2. Configure Environment

cp .env.example .env

Edit .env and add your API keys:

OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=...
API_KEY=sk-mcp-your-secret-key
JWT_SECRET=your-random-secret

3. Start Infrastructure

docker compose up -d postgres qdrant redis

4. Run Database Migrations

npm run db:migrate

5. Start the Server

Development mode:

npm run dev

Production mode:

npm run build
npm start

6. Start MCP Server (for MCP clients)

npm run mcp

API Endpoints

All endpoints require x-api-key header matching your API_KEY env var.

Method	Endpoint	Description
POST	`/chat`	Send query to orchestrator (auto-routes to agents)
POST	`/agent/ceo`	Ask CEO directly
POST	`/agent/cto`	Ask CTO directly
POST	`/agent/designer`	Ask Designer directly
POST	`/agent/sales`	Ask Sales directly
POST	`/agent/marketing`	Ask Marketing directly
POST	`/agent/qa`	Ask QA directly
POST	`/memory`	Save or search memory
GET	`/projects`	List all projects
GET	`/projects/:id`	Get project details
GET	`/tasks`	List tasks
GET	`/health`	Health check

Chat Example

curl -X POST http://localhost:3001/chat \
  -H "Content-Type: application/json" \
  -H "x-api-key: sk-mcp-your-secret-key" \
  -d '{"query": "I want to launch an AI SaaS for small businesses"}'

MCP Tools

When running as an MCP server (via npm run mcp), the following tools are exposed:

Tool	Description
`ask_ceo`	Business strategy advice
`ask_cto`	Technical architecture guidance
`ask_designer`	UI/UX and brand design
`ask_sales`	Sales and lead generation
`ask_marketing`	Marketing and growth
`ask_qa`	Code review and quality
`ask_all_agents`	Multi-agent collaboration
`create_project`	Create a new project
`save_memory`	Store long-term memory
`load_memory`	Retrieve memories
`search_documents`	Semantic document search
`generate_strategy`	Business strategy generation
`generate_ui`	UI design generation
`generate_architecture`	System architecture design
`generate_marketing_plan`	Marketing plan generation
`generate_sales_plan`	Sales plan generation
`review_project`	Full project review

Docker Deployment

Start everything with one command:

docker compose up --build

This starts:

app - The Multi-Agent MCP server
postgres - PostgreSQL with pgvector
qdrant - Vector database for embeddings
redis - Cache layer

Environment Variables

Variable	Required	Description
`API_KEY`	Yes	API authentication key
`JWT_SECRET`	Yes	JWT signing secret
`OPENAI_API_KEY`	No*	OpenAI API key
`ANTHROPIC_API_KEY`	No*	Anthropic API key
`GEMINI_API_KEY`	No*	Google Gemini API key
`DATABASE_URL`	No	PostgreSQL connection string
`QDRANT_URL`	No	Qdrant endpoint
`REDIS_URL`	No	Redis connection string

*At least one AI provider API key is required.

Project Structure

src/
├── agents/          # Agent implementations
│   ├── BaseAgent.ts # Abstract base class
│   ├── CEOAgent.ts
│   ├── CTOAgent.ts
│   ├── DesignerAgent.ts
│   ├── SalesAgent.ts
│   ├── MarketingAgent.ts
│   └── QAAgent.ts
├── orchestrator/    # Routing & workflow
│   ├── Router.ts    # Intent detection, agent selection
│   └── Workflow.ts  # Multi-agent execution, response merging
├── memory/          # Persistence layer
│   ├── postgres.ts  # PostgreSQL client
│   ├── qdrant.ts    # Qdrant vector client
│   ├── logger.ts    # Winston logger
│   └── migrate.ts   # DB migration runner
├── tools/           # MCP server
│   └── server.ts    # FastMCP SDK server with tool registration
├── config/          # Configuration
│   └── index.ts     # Env var loading
├── types/           # TypeScript types
│   └── index.ts     # All type definitions
└── index.ts         # Express REST API entry point

Adding a New Agent

Create src/agents/YourAgent.ts extending BaseAgent
Add your agent role to AgentRole enum in src/types/index.ts
Add a system prompt in SYSTEM_PROMPTS in src/types/index.ts
Register the agent in Router.ts constructor
Add MCP tool in src/tools/server.ts
Add REST endpoint in src/index.ts
Add SQL seed in sql/schema.sql

License

MIT

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quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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