MESH - Model Exchange Server Handler

A comprehensive multi-protocol AI agent server that transforms any AI application into a sophisticated virtual assistant with email management, contact management, and professional networking capabilities.

🎯 Overview

MESH (Model Exchange Server Handler) is a cutting-edge multi-protocol AI agent server that bridges the gap between AI applications and professional workflows. It provides seamless integration with three major AI agent protocols:

MCP (Model Context Protocol) - Direct AI application integration via STDIO
A2A (Agent-to-Agent) - Multi-agent collaboration via HTTP/WebSocket
ACP (Agent Communication Protocol) - Standardized agent interoperability via REST API

🚀 Key Features

📧 Professional Email Management - Create, edit, and manage professional emails with AI-powered templates
👥 Contact Database Operations - Search and retrieve contact information from comprehensive directories
🤝 Strategic Networking - Facilitate professional introductions and relationship building
🔄 Multi-Agent Collaboration - Coordinate with other AI agents for enhanced capabilities
⚡ Three-Protocol Support - MCP, A2A, and ACP protocols in a single server
🛠️ Production Ready - Comprehensive testing, configuration validation, and deployment scripts

🏗️ Architecture

MESH implements a sophisticated three-layer architecture that supports multiple AI agent protocols simultaneously:

┌─────────────────────────────────────────────────────────────────┐
│                    MESH Server Ecosystem                        │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │ MCP Server  │  │ A2A Server  │  │ ACP Server  │            │
│  │ (STDIO)     │  │ (HTTP/WS)   │  │ (REST API)  │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
│         │               │               │                      │
│         └───────────────┼───────────────┘                      │
│                         ▼                                      │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │              Shared Core Services                       │   │
│  │  • Email Management  • Contact Management              │   │
│  │  • Template System   • Agent Management                │   │
│  │  • Task Orchestration • Message Routing                │   │
│  └─────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────┘

Protocol Comparison

Protocol	Transport	Use Case	Primary Clients
MCP	STDIO	AI assistants, coding tools	Claude Desktop, Cursor, MCP Inspector
A2A	HTTP/WebSocket	Multi-agent workflows	A2A Inspector, Web applications
ACP	REST API	Enterprise integration	Custom clients, third-party systems

🚀 Quick Start

Prerequisites

Python 3.11+ - Modern Python with async support
uv - Fast Python package manager (auto-installed)
Git - For cloning the repository

One-Command Setup

# Clone and setup everything
git clone https://github.com/vishalm/agentic-protocol-demos.git
cd agentic-protocol-demos
./run.sh

This single command will:

✅ Check Python version and install uv if needed
✅ Create virtual environment and install dependencies
✅ Validate configuration and run tests
✅ Start the hybrid server (MCP + A2A)

Manual Setup

# Install uv package manager
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone repository
git clone https://github.com/vishalm/agentic-protocol-demos.git
cd agentic-protocol-demos

# Install dependencies
uv sync

# Run tests
uv run python test-mcp-functions.py
uv run python test-acp-functions.py

# Start servers
./run.sh start      # Hybrid server (MCP + A2A)
./run.sh start-acp  # ACP server only
./run.sh start-all  # All servers

🔧 Server Options

1. Simple MCP Server (Recommended for Testing)

File: mcp-server-test.py
Use Case: Testing with MCP Inspector, basic MCP integration
Features: Clean shutdown, no hanging issues, easy debugging
Command: python mcp-server-test.py

2. Enhanced MCP Server (Advanced Features)

File: mcp-server-enhanced.py
Use Case: Advanced MCP features with enhanced tools and capabilities
Features: Enhanced email tools, authentication, logging, sampling, elicitation
Command: uv run mcp dev mcp-server-enhanced.py

3. Hybrid Server (Production Ready)

File: hybrid_server.py
Use Case: Production deployment with both MCP and A2A protocols
Features: Multi-protocol support, subprocess management, graceful shutdown
Command: python hybrid_server.py or ./run.sh start

4. ACP Server (Enterprise Integration)

File: acp_server.py
Use Case: Multi-agent collaboration, standardized agent communication
Features: RESTful API, agent discovery, task management, message routing
Command: python acp_server.py or ./run.sh start-acp
Port: 8081 (http://127.0.0.1:8081)

🧪 Testing & Debugging

Project Status Check

# Check server status
./run.sh status

# Run comprehensive tests
./run.sh test

# Check specific components
./run.sh setup  # Full environment setup

MCP Inspector Testing

The MCP Inspector helps debug MCP protocol communication:

# Install MCP Inspector
pip install mcp-inspector

# Start MESH server
python mcp-server-test.py

# Launch MCP Inspector
mcp-inspector

Configuration for MCP Inspector:

Transport: STDIO
Command: python mcp-server-test.py (for basic server)
Command: uv run mcp dev mcp-server-enhanced.py (for enhanced server)
Working Directory: Your MESH project folder

A2A Inspector Testing

Test A2A protocol communication:

# Start A2A server
./run.sh start

# Access A2A Inspector
# Navigate to: http://127.0.0.1:8080
# Or use online: https://a2aprotocol.ai/inspector

ACP API Testing

Test ACP server functionality:

# Start ACP server
./run.sh start-acp

# Test with client
python acp_client.py

# Access API docs
# Navigate to: http://127.0.0.1:8081/docs

📊 Available Tools & Capabilities

MCP Tools

Tool	Description	Parameters
`write_email_draft`	Create professional email drafts	`recipient_email`, `subject`, `body`
`get_contact_info`	Search contact database	`name` (optional)
`suggest_email_template`	Get AI-powered email templates	`context`

A2A Methods

Method	Description	Use Case
`initialize`	Initialize agent connection	Setup and handshake
`capabilities`	Get agent capabilities	Discovery and planning
`write_email_draft`	Create email drafts	Professional communication
`get_contact_info`	Search contacts	Relationship management
`discover_agents`	Find other agents	Multi-agent coordination
`delegate_task`	Delegate to other agents	Workflow orchestration
`collaborate`	Initiate collaboration	Team coordination

ACP Endpoints

Endpoint	Method	Description
`/agents/register`	POST	Register new agents
`/agents`	GET	List registered agents
`/messages`	POST	Send messages to agents
`/tasks`	GET	List and monitor tasks
`/health`	GET	Server health check

🚀 Real-World Use Cases

1. Professional Email Management

Scenario: A business professional needs to send follow-up emails after networking events
MESH Solution: 
- MCP client requests email composition
- MESH orchestrates with A2A email writing agent
- A2A tone analysis agent ensures appropriate messaging
- A2A grammar check agent validates content
Result: Professionally crafted, contextually appropriate emails

2. Strategic Networking Automation

Scenario: Building and maintaining professional relationships at scale
MESH Solution:
- MCP client requests networking strategy
- MESH coordinates with A2A network analysis agent
- A2A opportunity identification agent finds prospects
- A2A calendar agent schedules follow-ups
Result: Automated networking pipeline with strategic follow-ups

3. Multi-Agent Customer Support

Scenario: Complex customer inquiries requiring multiple specialized agents
MESH Solution:
- MCP client receives customer request
- MESH routes to appropriate A2A agents (technical, billing, product)
- Agents collaborate to provide comprehensive solution
- MESH aggregates responses into unified answer
Result: Seamless customer experience with expert-level support

4. Content Creation Workflow

Scenario: Creating professional content requiring research, writing, and review
MESH Solution:
- MCP client requests content creation
- MESH coordinates research agent for information gathering
- Writing agent creates initial draft
- Review agent provides feedback and improvements
Result: High-quality, well-researched content delivered efficiently

🤖 ACP (Agent Communication Protocol) Integration

What is ACP?

The Agent Communication Protocol (ACP) is an open protocol for agent interoperability that solves the growing challenge of connecting AI agents, applications, and humans. ACP enables agents to communicate through a standardized RESTful API that supports:

All forms of modality - Text, images, audio, video, and custom formats
Synchronous and asynchronous communication - Flexible interaction patterns
Streaming interactions - Real-time communication capabilities
Both stateful and stateless operation - Adaptable to different use cases
Online and offline agent discovery - Robust agent management
Long running tasks - Support for complex workflows

Why ACP with MESH?

ACP addresses key challenges in modern AI systems:

Integration barriers - Connect different agents without custom solutions
Duplicated effort - Avoid rebuilding similar functionality
Scalability challenges - Point-to-point integrations don't scale
Inconsistent developer experience - Standardized patterns across frameworks

ACP Use Cases Enabled by MESH

1. Flexible Agent Replacement

As AI technology rapidly evolves, the best-performing agent changes frequently. ACP compatibility lets you seamlessly swap agents in production systems, even when built with different frameworks.

Example: Upgrade your translation agent from a custom model to the latest LLM, 
or replace your code generation tool with a specialized coding assistant - 
all without modifying integration points.

2. Multi-Agent Collaboration

Build sophisticated systems where specialized agents work as coordinated teams.

Example: A content creation workflow might involve:
- Topic research agent gathering information
- Writing agent crafting the draft  
- SEO optimization agent refining for search performance
Each agent excels in their domain while seamlessly coordinating through standardized handoffs.

3. Cross-Platform Integration

Connect agents across your technology stack - marketing automation, analytics platforms, development tools, and communication systems.

Example: When your monitoring agent detects a performance issue, it can automatically:
- Trigger your incident response agent to create tickets
- Notify teams through communication agents
- Coordinate with deployment systems to roll back changes

4. Inter-Company Partnerships

Enable secure agent collaboration between organizations.

Example: A logistics company's routing agent could work with a supplier's inventory 
agent to optimize delivery schedules, or a marketing agency's campaign agent could 
coordinate with a client's approval workflow agent for streamlined content reviews.

ACP Server Features

Core Capabilities

Agent Registration & Discovery - Register and discover available agents
Task Management - Send, monitor, and cancel tasks
Message Routing - Route messages between agents
State Management - Track task status and progress
Error Handling - Comprehensive error reporting and recovery

Supported Task Types

Email Draft Creation - Professional email composition
Contact Search - Database querying and filtering
Template Suggestions - AI-powered email templates
Custom Tasks - Extensible for any agent capability

API Endpoints

GET /health - Server health check
POST /agents/register - Register new agents
GET /agents - List all registered agents
POST /messages - Send messages to agents
GET /tasks - List and monitor tasks
DELETE /tasks/{id} - Cancel running tasks

Getting Started with ACP

1. Start the ACP Server

# Start the ACP server
python acp_server.py

# Server will be available at http://127.0.0.1:8081
# Interactive docs available at http://127.0.0.1:8081/docs

2. Test ACP Functionality

# Run ACP function tests
python test-acp-functions.py

# Test client-server communication
python acp_client.py

3. Explore the API

Health Check: curl http://127.0.0.1:8081/health
List Agents: curl http://127.0.0.1:8081/agents
API Documentation: Visit http://127.0.0.1:8081/docs

4. Register Your First Agent

import asyncio
from acp_client import ACPClient, EXAMPLE_AGENT_MANIFEST

async def register_agent():
    async with ACPClient() as client:
        result = await client.register_agent(EXAMPLE_AGENT_MANIFEST)
        print(f"Agent registered: {result}")

asyncio.run(register_agent())

ACP Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   MCP Client    │    │   ACP Server    │    │   ACP Agents    │
│                 │    │                 │    │                 │
│ • Email Tools   │◄──►│ • Task Router   │◄──►│ • Email Agent   │
│ • Contact Mgmt  │    │ • Message Bus   │    │ • Contact Agent │
│ • Templates     │    │ • State Mgmt    │    │ • Template Agent│
└─────────────────┘    └─────────────────┘    └─────────────────┘

Integration with Existing MCP Tools

ACP seamlessly integrates with your existing MCP tools:

MCP Tools → ACP Tasks: Convert MCP tool calls to ACP task messages
MCP Resources → ACP Artifacts: Share data and templates between protocols
MCP Prompts → ACP Agent Instructions: Use MCP prompts to configure ACP agents

🚀 Real-World Use Cases

1. Professional Email Management

Scenario: A business professional needs to send follow-up emails after networking events
MESH Solution: 
- MCP client requests email composition
- MESH orchestrates with A2A email writing agent
- A2A tone analysis agent ensures appropriate messaging
- A2A grammar check agent validates content
Result: Professionally crafted, contextually appropriate emails

2. Strategic Networking Automation

Scenario: Building and maintaining professional relationships at scale
MESH Solution:
- MCP client requests networking strategy
- MESH coordinates with A2A network analysis agent
- A2A opportunity identification agent finds prospects
- A2A calendar agent schedules follow-ups
Result: Automated networking pipeline with strategic follow-ups

3. Multi-Agent Customer Support

Scenario: Complex customer inquiries requiring multiple specialized agents
MESH Solution:
- MCP client receives customer request
- MESH routes to appropriate A2A agents (technical, billing, product)
- Agents collaborate to provide comprehensive solution
- MESH aggregates responses into unified answer
Result: Seamless customer experience with expert-level support

4. Content Creation Workflow

Scenario: Creating professional content requiring research, writing, and review
MESH Solution:
- MCP client requests content creation
- MESH coordinates research agent for information gathering
- Writing agent creates initial draft
- Review agent provides feedback and improvements
Result: High-quality, well-researched content delivered efficiently

🔄 Communication Flow Diagrams

MCP (Model Context Protocol) Communication Flow

sequenceDiagram
    participant AI as AI Application
    participant MESH as MESH MCP Server
    participant Email as Email Manager
    participant Contact as Contact Manager
    participant Network as Network Manager

    AI->>MESH: MCP Request (tools/list)
    MESH->>AI: Available Tools Response
    
    AI->>MESH: MCP Request (tools/call)
    Note over MESH: Parse tool call
    alt Email Management
        MESH->>Email: Execute email operation
        Email->>MESH: Email result
    else Contact Management
        MESH->>Contact: Execute contact operation
        Contact->>MESH: Contact result
    else Network Management
        MESH->>Network: Execute network operation
        Network->>MESH: Network result
    end
    
    MESH->>AI: MCP Response with result
    Note over AI: Process and display result

ACP (Agent Communication Protocol) Communication Flow

sequenceDiagram
    participant Client as ACP Client
    participant ACPServer as MESH ACP Server
    participant AgentManager as Agent Manager
    participant TaskProcessor as Task Processor
    participant ExternalAgents as External ACP Agents

    Client->>ACPServer: POST /agents/register
    ACPServer->>AgentManager: Register agent
    AgentManager->>ACPServer: Agent registered
    ACPServer->>Client: Registration success

    Client->>ACPServer: POST /messages (task)
    Note over ACPServer: Process task message
    ACPServer->>TaskProcessor: Route task
    TaskProcessor->>ExternalAgents: Execute task
    
    ExternalAgents->>TaskProcessor: Task result
    TaskProcessor->>ACPServer: Process result
    ACPServer->>Client: Response message
    
    Note over Client: Handle response
    Client->>ACPServer: GET /tasks/{id}
    ACPServer->>Client: Task status

A2A (Agent-to-Agent) Communication Flow

sequenceDiagram
    participant User as User
    participant A2AInspector as A2A Inspector
    participant MESH as MESH A2A Server
    participant AgentManager as Agent Manager
    participant TaskOrchestrator as Task Orchestrator
    participant ExternalAgents as External A2A Agents

    User->>A2AInspector: Connect to MESH
    A2AInspector->>MESH: GET /.well-known/agent-card
    MESH->>A2AInspector: Agent Card Response
    
    User->>A2AInspector: Send message
    A2AInspector->>MESH: POST / (message/send)
    Note over MESH: Process message with JSON-RPC 2.0
    
    MESH->>AgentManager: Discover available agents
    AgentManager->>MESH: Agent list
    
    MESH->>TaskOrchestrator: Orchestrate workflow
    TaskOrchestrator->>ExternalAgents: Coordinate tasks
    
    ExternalAgents->>TaskOrchestrator: Task results
    TaskOrchestrator->>MESH: Workflow completion
    
    MESH->>A2AInspector: SendMessageResponse
    Note over A2AInspector: Validate response schema
    A2AInspector->>User: Display result

MESH Complete Architecture (MCP + A2A + ACP)

graph TB
    subgraph "MESH Server Ecosystem"
        subgraph "MCP Layer"
            MCP[FastMCP Server]
            MCPTools[MCP Tools Registry]
            MCPHandler[MCP Handler]
        end
        
        subgraph "A2A Layer"
            A2AServer[A2A Protocol Server]
            A2AHandler[A2A Handler]
            JSONRPC[JSON-RPC 2.0]
        end
        
        subgraph "ACP Layer"
            ACPServer[ACP Server]
            ACPHandler[ACP Handler]
            RESTAPI[REST API]
        end
        
        subgraph "Core Services"
            EmailService[Email Service]
            ContactService[Contact Service]
            NetworkService[Network Service]
            TemplateService[Template Service]
        end
        
        subgraph "Agent Management"
            AgentManager[Agent Manager]
            TaskOrchestrator[Task Orchestrator]
            MessageRouter[Message Router]
        end
    end
    
    subgraph "External Systems"
        AIApp[AI Application]
        A2AInspector[A2A Inspector]
        ACPClient[ACP Client]
        ExternalAgents[External Agents]
    end
    
    %% MCP Connections
    AIApp <--> MCP
    MCP --> MCPTools
    MCPTools --> CoreServices
    
    %% A2A Connections
    A2AInspector <--> A2AServer
    A2AServer --> AgentManager
    A2AServer --> TaskOrchestrator
    
    %% ACP Connections
    ACPClient <--> ACPServer
    ACPServer --> AgentManager
    ACPServer --> TaskOrchestrator
    ACPServer --> MessageRouter
    
    %% Cross-Protocol Integration
    AgentManager --> ExternalAgents
    TaskOrchestrator --> ExternalAgents
    MessageRouter --> ExternalAgents
    
    %% Service Integration
    AgentManager --> CoreServices
    TaskOrchestrator --> CoreServices
    MessageRouter --> CoreServices
    
    %% Protocol Handlers
    MCPHandler --> MCP
    A2AHandler --> A2AServer
    ACPHandler --> ACPServer
    RESTAPI --> ACPServer
    JSONRPC --> A2AServer

Protocol Integration & Data Flow

flowchart LR
    subgraph "Client Applications"
        MCPClient[MCP Client<br/>AI Applications]
        A2AClient[A2A Client<br/>Inspector Tools]
        ACPClient[ACP Client<br/>Agent Systems]
    end
    
    subgraph "MESH Protocol Layer"
        MCP[MCP Server<br/>STDIO Transport]
        A2A[A2A Server<br/>HTTP/WebSocket]
        ACP[ACP Server<br/>REST API]
    end
    
    subgraph "Shared Services"
        Email[Email Service]
        Contact[Contact Service]
        Template[Template Service]
        Agent[Agent Manager]
    end
    
    subgraph "Data Sources"
        Directory[Contact Directory]
        Templates[Email Templates]
        Prompts[System Prompts]
    end
    
    %% Client to Protocol connections
    MCPClient --> MCP
    A2AClient --> A2A
    ACPClient --> ACP
    
    %% Protocol to Services connections
    MCP --> Email
    MCP --> Contact
    MCP --> Template
    MCP --> Agent
    
    A2A --> Email
    A2A --> Contact
    A2A --> Template
    A2A --> Agent
    
    ACP --> Email
    ACP --> Contact
    ACP --> Template
    ACP --> Agent
    
    %% Services to Data connections
    Email --> Templates
    Contact --> Directory
    Template --> Templates
    Agent --> Prompts
    
    %% Cross-protocol communication
    MCP -.-> ACP
    A2A -.-> ACP
    ACP -.-> A2A
    
    style MCP fill:#e1f5fe
    style A2A fill:#f3e5f5
    style ACP fill:#e8f5e8
    style Email fill:#fff3e0
    style Contact fill:#fff3e0
    style Template fill:#fff3e0
    style Agent fill:#fff3e0

🏗️ Architecture Overview

Three-Protocol Architecture

MESH now supports three complementary protocols that work together to provide comprehensive AI agent capabilities:

1. MCP (Model Context Protocol)

Purpose: Direct AI application integration
Transport: STDIO (standard input/output)
Use Case: AI assistants, coding tools, desktop applications
Features: Tool calling, resource access, prompt management

2. A2A (Agent-to-Agent)

Purpose: Multi-agent collaboration and orchestration
Transport: HTTP + WebSocket
Use Case: Complex workflows, agent coordination, real-time communication
Features: Agent discovery, message routing, workflow orchestration

3. ACP (Agent Communication Protocol)

Purpose: Standardized agent interoperability
Transport: RESTful HTTP API
Use Case: Cross-platform integration, third-party agents, enterprise systems
Features: Agent registration, task management, message history

How They Work Together

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   MCP Client    │    │   A2A Client    │    │   ACP Client    │
│                 │    │                 │    │                 │
│ • Claude        │    │ • Inspector     │    │ • Agent System  │
│ • Cursor        │    │ • Web Tools     │    │ • Enterprise    │
│ • AI Apps       │    │ • Real-time     │    │ • Integration   │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────────────────────────────────────────────────────┐
│                    MESH Server Ecosystem                        │
│                                                                 │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │
│  │ MCP Server  │  │ A2A Server  │  │ ACP Server  │            │
│  │ (STDIO)     │  │ (HTTP/WS)   │  │ (REST API)  │            │
│  └─────────────┘  └─────────────┘  └─────────────┘            │
│         │               │               │                      │
│         └───────────────┼───────────────┘                      │
│                         ▼                                      │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │              Shared Core Services                       │   │
│  │  • Email Management  • Contact Management              │   │
│  │  • Template System   • Agent Management                │   │
│  │  • Task Orchestration • Message Routing                │   │
│  └─────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────┘

Protocol Benefits

🔄 Interoperability: Agents built for any protocol can work together
📈 Scalability: Add new protocols without changing existing ones
🛠️ Flexibility: Choose the right protocol for your use case
🔗 Integration: Seamless communication between different systems
📊 Monitoring: Unified view of all agent activities

🛠️ Prerequisites

Python 3.11+ - Modern Python with async support
uv - Fast Python package manager
Git - For cloning the repository
Web Browser - For accessing inspector tools

📦 Installation & Setup

Step 1: Install uv Package Manager

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

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# Verify installation
uv --version

Step 2: Clone and Setup Project

# Clone the repository
git clone https://github.com/vishalm/agentic-protocol-demos.git
cd agentic-protocol-demos

# Install dependencies
uv sync

# Activate virtual environment
source .venv/bin/activate  # macOS/Linux
# .venv\Scripts\activate   # Windows

🚀 Quick Start Guide

Option 1: One-Command Setup (Recommended)

# Start everything with one command
./run.sh start

This will:

✅ Check Python version and dependencies
✅ Install required packages
✅ Start both MCP and A2A servers
✅ Display connection information

Option 2: Manual Setup

# Start hybrid server manually
python hybrid_server.py

# Or start individual servers
python a2a_server.py      # A2A server only
python mcp_server.py      # MCP server only

🔧 Configuration

MCP Configuration

Create mcp-config.json in your AI application:

{
  "mcpServers": {
    "MESH": {
      "command": "/path/to/uv",
      "args": [
        "--directory",
        "/path/to/agentic-protocol-demos",
        "run",
        "--with",
        "mcp",
        "--with",
        "fastmcp",
        "python",
        "hybrid_server.py"
      ]
    }
  }
}

A2A Configuration

The A2A server automatically generates its agent card at /.well-known/agent-card:

{
  "name": "mesh_agent",
  "description": "Professional email management and networking assistant with multi-agent collaboration capabilities.",
  "protocolVersion": "0.3.0",
  "preferredTransport": "JSONRPC",
  "capabilities": {
    "streaming": false
  },
  "skills": [
    {
      "id": "email_management",
      "name": "Email Composition & Management",
      "description": "Create professional emails and manage email templates"
    }
  ]
}

🧪 Testing & Debugging

Project Status Check

# Check server status
./run.sh status

# Check specific components
./run.sh check-python
./run.sh check-deps
./run.sh check-structure

Server Management

# Start servers
./run.sh start

# Stop servers
./run.sh stop

# Restart servers
./run.sh restart

# View logs
./run.sh logs

🔍 MCP Inspector Testing

MCP Inspector Setup

The MCP Inspector helps debug MCP protocol communication and tool calls.

1. Install MCP Inspector

# Install globally
pip install mcp-inspector

# Or use with uv
uv run mcp-inspector

2. Configure MCP Inspector

When MCP Inspector launches:

Select Transport: Choose "STDIO" transport
Set Command: Enter python mcp-server-test.py
Set Working Directory: Navigate to your MESH project folder
Connect: Click "Connect" to establish connection

3. Test MCP Tools

List available tools
Test individual tool calls
Monitor tool execution
Debug tool responses

MCP Inspector in Action

The MCP Inspector provides a comprehensive interface for testing and debugging MCP protocol communication. Here are key screenshots showing the MCP Inspector working with MESH:

1. Initial Connection Setup

MCP Inspector Setup Configuring the MCP Inspector to connect to the MESH server via STDIO transport

2. Resources Management

MESH Resources Viewing available resources including email templates and contact directory

3. Prompts Configuration

MESH Prompts Accessing the MESH assistant prompt template and global instructions

4. Tools and Functions

MESH Tools Exploring available tools: email draft creation, contact management, and template suggestions

5. Server Status and Logs

MESH Server Logs Monitoring server activity and connection status in real-time

MCP Inspector Features

Tool Discovery: Automatically lists all available MCP tools
Resource Management: Browse and access MCP resources
Prompt Configuration: Set up system prompts and instructions
Real-time Logging: Monitor all MCP communication
Error Debugging: Identify and fix protocol issues
Tool Testing: Execute individual tool calls for validation

Running MCP Inspector with MESH

1. Start MESH Server First

# Test the server functions first
python test-mcp-functions.py

# Start the MCP server
python mcp-server-test.py

2. Launch MCP Inspector

# Install and run MCP Inspector
pip install mcp-inspector
mcp-inspector

# Or use uv for faster installation
uv run mcp-inspector

3. Configure MCP Inspector

When MCP Inspector launches:

Select Transport: Choose "STDIO" transport
Set Command: Enter python mcp-server-test.py
Set Working Directory: Navigate to your MESH project folder
Connect: Click "Connect" to establish connection

4. Verify Connection

Successful connection will show:

✅ MCP Server Connected
📋 Available Tools: List of MCP functions
📚 Resources: Available data sources
⚙️ Prompts: System configuration templates

5. Test MCP Tools

In MCP Inspector, you can now:

Browse available tools
Execute tool calls
View tool responses
Monitor server logs
Test resource access

6. Stop MCP Inspector

Close the MCP Inspector application
Use Ctrl+C in the terminal running MESH server

🧪 Testing ACP Server

Quick Start Testing

1. Test ACP Functions

# Run comprehensive ACP tests
python test-acp-functions.py

# Expected output: All 6 tests should pass

2. Start ACP Server

# Start the ACP server
python acp_server.py

# Server will be available at http://127.0.0.1:8081
# Interactive API docs at http://127.0.0.1:8081/docs

3. Test Client-Server Communication

# In a new terminal, run the ACP client demo
python acp_client.py

# This will demonstrate:
# - Agent registration
# - Task sending and processing
# - Message routing
# - Status monitoring

4. Manual API Testing

# Health check
curl http://127.0.0.1:8081/health

# List agents
curl http://127.0.0.1:8081/agents

# Send a test task
curl -X POST http://127.0.0.1:8081/messages \
  -H "Content-Type: application/json" \
  -d '{
    "type": "task",
    "sender": "test-client",
    "task_type": "email_draft",
    "parameters": {
      "recipient_email": "test@example.com",
      "subject": "Test Email",
      "body": "This is a test email."
    }
  }'

ACP Server Features Demonstrated

Agent Management

✅ Agent registration and discovery
✅ Capability and task type specification
✅ Metadata and contact information

Task Processing

✅ Email draft creation
✅ Contact search and filtering
✅ Template suggestions
✅ Priority and timeout handling

Message Routing

✅ Task message handling
✅ Response message generation
✅ Error message creation
✅ Message history tracking

State Management

✅ Task status tracking (pending → running → completed/failed)
✅ Task cancellation support
✅ Comprehensive error handling

🔍 Inspector Tools Setup

A2A Inspector Setup

The A2A Inspector is a web-based tool for testing and debugging A2A protocol communication.

1. Access A2A Inspector

Navigate to: http://127.0.0.1:5001 (if running locally)
Or use the online version: A2A Inspector

2. Connect to MESH

Enter your MESH server URL: http://127.0.0.1:8080
Click Connect
Verify the agent card loads successfully

3. Test Communication

Use the chat interface to send messages
Monitor the debug console for raw JSON-RPC communication
Check validation results for protocol compliance

4. Debug Console Features

Raw JSON View: See exact protocol messages
Validation Results: Check for protocol compliance
Error Details: Identify and fix protocol issues
Real-time Logging: Monitor all communication

MCP Inspector Setup

The MCP Inspector helps debug MCP protocol communication and tool calls.

1. Install MCP Inspector

# Install globally
pip install mcp-inspector

# Or use with uv
uv run mcp-inspector

2. Configure MCP Inspector

# Create config file
mcp-inspector init

# Edit config to point to MESH
# Add your MESH server configuration

3. Test MCP Tools

List available tools
Test individual tool calls
Monitor tool execution
Debug tool responses

MCP Inspector in Action

The MCP Inspector provides a comprehensive interface for testing and debugging MCP protocol communication. Here are key screenshots showing the MCP Inspector working with MESH:

1. Initial Connection Setup

Configuring the MCP Inspector to connect to the MESH server via STDIO transport

2. Resources Management

Viewing available resources including email templates and contact directory

3. Prompts Configuration

Accessing the MESH assistant prompt template and global instructions

4. Tools and Functions

Exploring available tools: email draft creation, contact management, and template suggestions

5. Server Status and Logs

Monitoring server activity and connection status in real-time

MCP Inspector Features

Tool Discovery: Automatically lists all available MCP tools
Resource Management: Browse and access MCP resources
Prompt Configuration: Set up system prompts and instructions
Real-time Logging: Monitor all MCP communication
Error Debugging: Identify and fix protocol issues
Tool Testing: Execute individual tool calls for validation

Running MCP Inspector with MESH

1. Start MESH Server First

# Start the hybrid server (MCP + A2A)
./run.sh start

# Or start server only (no tests)
./run.sh start-only

2. Verify MCP Server Status

# Check if servers are running
./run.sh status

# Expected output:
# ✅ MESH Hybrid Server is running
#   - MCP: STDIO transport (active)
#   - A2A: http://127.0.0.1:8080

3. Launch MCP Inspector

# Install and run MCP Inspector
pip install mcp-inspector
mcp-inspector

# Or use uv for faster installation
uv run mcp-inspector

4. Configure MCP Inspector

When MCP Inspector launches:

Select Transport: Choose "STDIO" transport
Set Command: Enter python hybrid_server.py
Set Working Directory: Navigate to your MESH project folder
Connect: Click "Connect" to establish connection

5. Verify Connection

Successful connection will show:

✅ MCP Server Connected
📋 Available Tools: List of MCP functions
📚 Resources: Available data sources
⚙️ Prompts: System configuration templates

6. Test MCP Tools

# In MCP Inspector, you can now:
# - Browse available tools
# - Execute tool calls
# - View tool responses
# - Monitor server logs
# - Test resource access

7. Stop MCP Inspector

Close the MCP Inspector application
Use Ctrl+C in the terminal running MESH server
Or run ./run.sh stop to stop all servers

📊 Project Structure

agentic-protocol-demos/
├── mcp-server-test.py        # Main MESH MCP server
├── test-mcp-functions.py     # MCP function testing script
├── validate-config.py         # MCP configuration generator
├── acp_server.py             # ACP (Agent Communication Protocol) server
├── acp_client.py             # ACP client for testing and integration
├── test-acp-functions.py     # ACP function testing script
├── hybrid_server.py          # Hybrid server (MCP + A2A) - advanced usage
├── a2a_server.py            # A2A protocol server
├── agent_manager.py          # Agent discovery and management
├── task_orchestrator.py      # Workflow orchestration
├── a2a_config.py             # A2A configuration
├── agent_capabilities.py     # Agent skill definitions
├── run.sh                    # One-command setup and management
├── mcp-config.json           # MCP configuration example
├── pyproject.toml            # Project dependencies
├── requirements.txt           # Python requirements
├── prompts/                  # MESH assistant prompt templates
│   └── mesh.md               # Main prompt template
├── email-examples/           # Email template examples
│   ├── 3-way-intro.md        # 3-way introduction template
│   └── call-follow-up.md     # Call follow-up template
├── directory.csv             # Contact directory
└── resources/                # Documentation images
    ├── A2A-1.png            # A2A Inspector screenshot
    ├── A2A-2.png            # Chat interface screenshot
    └── A2A-3.png            # Debug console screenshot

🚨 Troubleshooting

Common Issues

Port Already in Use

# Check what's using the port
lsof -i :8080

# Kill conflicting processes
./run.sh stop

# Or use different port
export A2A_PORT=8081
./run.sh start

A2A Inspector Connection Issues

Verify MESH server is running: ./run.sh status
Check firewall settings
Ensure correct port (8080 or 8081)
Verify agent card endpoint: curl http://127.0.0.1:8080/.well-known/agent-card

MCP Integration Issues

Verify MCP configuration path
Check Python environment activation
Ensure all dependencies are installed: uv sync
Test with MCP Inspector tool

Debug Mode

# Enable verbose logging
export LOG_LEVEL=DEBUG
./run.sh start

# View real-time logs
./run.sh logs

# Check server health
curl http://127.0.0.1:8080/health

🔗 Integration Examples

Claude Desktop Integration

Add to your Claude Desktop configuration:

Basic MCP Server:

{
  "mcpServers": {
    "MESH": {
      "command": "/path/to/uv",
      "args": [
        "--directory",
        "/path/to/agentic-protocol-demos",
        "run",
        "--with", "mcp",
        "--with", "fastmcp",
        "python",
        "mcp-server-test.py"
      ]
    }
  }
}

Enhanced MCP Server:

{
  "mcpServers": {
    "MESH-Enhanced": {
      "command": "/path/to/uv",
      "args": [
        "--directory",
        "/path/to/agentic-protocol-demos",
        "run",
        "mcp",
        "dev",
        "mcp-server-enhanced.py"
      ]
    }
  }
}

Cursor Integration

Add to your Cursor MCP configuration:

Basic MCP Server:

{
  "mcpServers": {
    "MESH": {
      "command": "/path/to/uv",
      "args": [
        "--directory",
        "/path/to/agentic-protocol-demos",
        "run",
        "--with", "mcp",
        "--with", "fastmcp",
        "python",
        "mcp-server-test.py"
      ]
    }
  }
}

Enhanced MCP Server:

{
  "mcpServers": {
    "MESH-Enhanced": {
      "command": "/path/to/uv",
      "args": [
        "--directory",
        "/path/to/agentic-protocol-demos",
        "run",
        "mcp",
        "dev",
        "mcp-server-enhanced.py"
      ]
    }
  }
}

Custom Client Integration

import httpx

# A2A Protocol
async with httpx.AsyncClient() as client:
    response = await client.post(
        "http://127.0.0.1:8080/",
        json={
            "jsonrpc": "2.0",
            "id": "1",
            "method": "write_email_draft",
            "params": {
                "recipient": "colleague@company.com",
                "subject": "Project Update",
                "body": "Hi there, here's the latest update..."
            }
        }
    )

# ACP Protocol
async with httpx.AsyncClient() as client:
    response = await client.post(
        "http://127.0.0.1:8081/messages",
        json={
            "type": "task",
            "sender": "my-client",
            "task_type": "email_draft",
            "parameters": {
                "recipient_email": "colleague@company.com",
                "subject": "Project Update",
                "body": "Hi there, here's the latest update..."
            }
        }
    )

📁 Project Structure

agentic-protocol-demos/
├── 📄 Core Servers
│   ├── mcp-server-test.py        # Simple MCP server (testing)
│   ├── hybrid_server.py          # Hybrid server (MCP + A2A)
│   ├── a2a_server.py            # A2A protocol server
│   └── acp_server.py            # ACP protocol server
│
├── 🧠 Agent Management
│   ├── agent_manager.py          # Agent discovery and management
│   ├── task_orchestrator.py      # Workflow orchestration
│   ├── agent_capabilities.py     # Agent skill definitions
│   └── a2a_config.py            # A2A configuration
│
├── 🧪 Testing & Validation
│   ├── test-mcp-functions.py     # MCP function testing
│   ├── test-acp-functions.py     # ACP function testing
│   ├── acp_client.py            # ACP client for testing
│   └── validate-config.py        # Configuration generator
│
├── 📚 Resources & Templates
│   ├── prompts/
│   │   └── mesh.md              # Main prompt template
│   ├── email-examples/
│   │   ├── 3-way-intro.md       # Introduction template
│   │   └── call-follow-up.md    # Follow-up template
│   └── directory.csv            # Contact directory
│
├── ⚙️ Configuration & Setup
│   ├── run.sh                   # One-command setup script
│   ├── pyproject.toml           # Project dependencies
│   ├── requirements.txt         # Python requirements
│   └── mcp-config.json          # MCP configuration example
│
└── 📖 Documentation
    ├── README.md                # This file
    ├── LICENSE                  # MIT License
    └── resources/               # Documentation images

🚨 Troubleshooting

Common Issues

Port Already in Use

# Check what's using the port
lsof -i :8080
lsof -i :8081

# Stop conflicting processes
./run.sh stop

# Or use different ports
export A2A_PORT=8082
export ACP_PORT=8083
./run.sh start

Python Version Issues

# Check Python version
python3 --version

# Install Python 3.11+ if needed
# macOS: brew install python@3.11
# Ubuntu: sudo apt install python3.11

Dependency Issues

# Clean install
rm -rf .venv
uv sync

# Or use pip fallback
pip install -r requirements.txt

MCP Integration Issues

# Test MCP functions first
python test-mcp-functions.py

# Generate correct configuration
python validate-config.py

# Test with MCP Inspector
pip install mcp-inspector
mcp-inspector

Debug Mode

# Enable verbose logging
export LOG_LEVEL=DEBUG
./run.sh start

# View real-time logs
./run.sh status

# Check server health
curl http://127.0.0.1:8080/health  # A2A
curl http://127.0.0.1:8081/health  # ACP

🔗 API Reference

A2A Endpoints

Endpoint	Method	Description
`/`	POST	Main A2A protocol endpoint
`/.well-known/agent-card`	GET	Agent capabilities and skills
`/health`	GET	Server health check
`/ws`	WebSocket	Real-time communication

MCP Tools

Tool	Description	Parameters
`email_compose`	Create professional emails	`subject`, `recipient`, `template`
`contact_search`	Search contact database	`query`, `filters`
`network_analyze`	Analyze networking opportunities	`context`, `goals`

🤝 Contributing

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Commit changes: git commit -m 'Add amazing feature'
Push to branch: git push origin feature/amazing-feature
Open a Pull Request

Development Setup

# Clone your fork
git clone https://github.com/yourusername/agentic-protocol-demos.git
cd agentic-protocol-demos

# Setup development environment
./run.sh setup

# Run tests
./run.sh test

# Start development server
./run.sh start

📄 License

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

🙏 Acknowledgments

MCP Community - For the Model Context Protocol specification
A2A Protocol - For the Agent-to-Agent communication standards
ACP Protocol - For the Agent Communication Protocol specification
FastAPI - For the robust web framework
FastMCP - For the efficient MCP implementation
uv - For the fast Python package manager

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Documentation: Project Wiki

Ready to transform your AI applications with MESH? Start with ./run.sh and explore the future of AI collaboration! 🚀

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
email-examples		email-examples
prompts		prompts
resources		resources
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
a2a_config.py		a2a_config.py
a2a_server.py		a2a_server.py
acp_client.py		acp_client.py
acp_server.py		acp_server.py
agent_capabilities.py		agent_capabilities.py
agent_manager.py		agent_manager.py
directory.csv		directory.csv
hybrid_server.py		hybrid_server.py
mcp-config.json		mcp-config.json
mcp-server-enhanced.py		mcp-server-enhanced.py
mcp-server-test.py		mcp-server-test.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
run.sh		run.sh
task_orchestrator.py		task_orchestrator.py
test-acp-functions.py		test-acp-functions.py
test-enhanced-mcp-functions.py		test-enhanced-mcp-functions.py
test-mcp-functions.py		test-mcp-functions.py
uv.lock		uv.lock
validate-config.py		validate-config.py

License

vishalm/agentic-protocol-demos

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Latest commit

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MESH - Model Exchange Server Handler

🎯 Overview

🚀 Key Features

🏗️ Architecture

Protocol Comparison

🚀 Quick Start

Prerequisites

One-Command Setup

Manual Setup

🔧 Server Options

1. Simple MCP Server (Recommended for Testing)

2. Enhanced MCP Server (Advanced Features)

3. Hybrid Server (Production Ready)

4. ACP Server (Enterprise Integration)

🧪 Testing & Debugging

Project Status Check

MCP Inspector Testing

A2A Inspector Testing

ACP API Testing

📊 Available Tools & Capabilities

MCP Tools

A2A Methods

ACP Endpoints

🚀 Real-World Use Cases

1. Professional Email Management

2. Strategic Networking Automation

3. Multi-Agent Customer Support

4. Content Creation Workflow

🤖 ACP (Agent Communication Protocol) Integration

What is ACP?

Why ACP with MESH?

ACP Use Cases Enabled by MESH

1. Flexible Agent Replacement

2. Multi-Agent Collaboration

3. Cross-Platform Integration

4. Inter-Company Partnerships

ACP Server Features

Core Capabilities

Supported Task Types

API Endpoints

Getting Started with ACP

1. Start the ACP Server

2. Test ACP Functionality

3. Explore the API

4. Register Your First Agent

ACP Architecture

Integration with Existing MCP Tools

🚀 Real-World Use Cases

1. Professional Email Management

2. Strategic Networking Automation

3. Multi-Agent Customer Support

4. Content Creation Workflow

🔄 Communication Flow Diagrams

MCP (Model Context Protocol) Communication Flow

ACP (Agent Communication Protocol) Communication Flow

A2A (Agent-to-Agent) Communication Flow

MESH Complete Architecture (MCP + A2A + ACP)

Protocol Integration & Data Flow

🏗️ Architecture Overview

Three-Protocol Architecture

1. MCP (Model Context Protocol)

2. A2A (Agent-to-Agent)

3. ACP (Agent Communication Protocol)

How They Work Together

Protocol Benefits

🛠️ Prerequisites

📦 Installation & Setup

Step 1: Install uv Package Manager

Step 2: Clone and Setup Project

🚀 Quick Start Guide

Option 1: One-Command Setup (Recommended)

Option 2: Manual Setup

🔧 Configuration

MCP Configuration