AgentNexus Agents as MCP Clients/Servers

Transforming AgentNexus agents into MCP clients and servers would be a powerful enhancement that enables them to seamlessly collaborate with external MCP-compatible systems. This approach focuses on making your existing agents "MCP-aware" without needing to completely rebuild their internal architecture.

Core Implementation Strategy

Agent as MCP Server

When an AgentNexus agent functions as an MCP server, it can:

Expose its capabilities to external systems
Accept requests to perform actions
Share resources and context
Execute workflows on behalf of clients

from agentnexus.base_types import AgentConfig
from agentnexus.mcp.server import McpServer, McpServerTransport

class McpEnabledAgent:
    def __init__(self, agent_config: AgentConfig):
        self.agent_config = agent_config
        
        # Create MCP server for this agent
        self.mcp_server = McpServer(
            name=agent_config.name,
            version=agent_config.version,
            capabilities={
                # Define capabilities based on agent actions
                "tools": self._get_tools_from_actions(),
                "resources": self._get_resource_capabilities()
            }
        )
        
        # Register handlers for MCP requests
        self._register_handlers()
        
    def _register_handlers(self):
        # Register a handler for each agent action
        for action in self.agent_config.actions:
            self.mcp_server.set_request_handler(
                action.name,
                lambda request, extra, action=action: self._execute_action(action, request, extra)
            )
            
    async def _execute_action(self, action, request, extra):
        """Execute an agent action in response to MCP request"""
        # Convert MCP request to action input
        action_input = self._convert_to_action_input(request.params, action)
        
        # Execute the action
        result = await action.handler(action_input)
        
        # Convert action result to MCP response
        return self._convert_to_mcp_response(result)
        
    def start_server(self, port=9000):
        """Start the MCP server on the specified port"""
        transport = McpServerTransport(port=port)
        return self.mcp_server.connect(transport)

Agent as MCP Client

When an AgentNexus agent functions as an MCP client, it can:

Discover and connect to external MCP servers
Request actions from other agents
Access external resources and contexts
Incorporate external capabilities into its workflows

from agentnexus.mcp.client import McpClient, McpClientTransport

class McpClientAgent:
    def __init__(self, agent_config: AgentConfig):
        self.agent_config = agent_config
        self.mcp_clients = {}  # Map of server_id to client
        
    async def connect_to_server(self, server_url, server_id=None):
        """Connect to an external MCP server"""
        client = McpClient()
        transport = McpClientTransport(server_url)
        
        # Initialize connection
        await client.connect(transport)
        
        # Store client reference
        server_id = server_id or server_url
        self.mcp_clients[server_id] = client
        return client
        
    async def discover_capabilities(self, server_id):
        """Discover capabilities of connected server"""
        client = self.mcp_clients.get(server_id)
        if not client:
            raise ValueError(f"Not connected to server {server_id}")
            
        # Request capabilities
        initialize_result = await client.initialize()
        return initialize_result.capabilities
        
    async def request_action(self, server_id, action_name, params=None):
        """Request an action from an external MCP server"""
        client = self.mcp_clients.get(server_id)
        if not client:
            raise ValueError(f"Not connected to server {server_id}")
            
        # Send action request
        response = await client.send_request({
            "method": action_name,
            "params": params or {}
        })
        
        return response

Enabling Multi-Agent Collaboration

With MCP support, AgentNexus agents can participate in rich collaborative scenarios with external systems:

1. Capability Advertisement and Discovery

Agents can advertise their capabilities through MCP's initialization messages:

# Agent capability advertisement
mcp_capabilities = {
    "tools": {
        "code-generation": {
            "version": "1.0.0",
            "languages": ["python", "javascript", "typescript"]
        },
        "code-analysis": {
            "version": "1.0.0",
            "analysis_types": ["security", "performance", "quality"]
        }
    },
    "resources": {
        "schemes": ["code", "documentation"]
    }
}

Other systems can discover these capabilities and decide how to use the agent:

# Discover agent capabilities
client = await McpClient.connect("http://localhost:9000")
initialize_result = await client.initialize()

# Check if agent can generate Python code
python_capable = (
    "tools" in initialize_result.capabilities and
    "code-generation" in initialize_result.capabilities["tools"] and
    "python" in initialize_result.capabilities["tools"]["code-generation"].get("languages", [])
)

2. Context Sharing

Agents can share context with external systems to create a common understanding:

# Create a shared context
context_id = "ctx-code-review-123"
context_data = {
    "code": {
        "language": "python",
        "filename": "main.py",
        "content": "def hello():\n    print('Hello, world!')"
    },
    "requirements": {
        "performance": True,
        "security": True
    }
}

# Share context with external system
await agent.mcp_client.send_request({
    "method": "createContext",
    "params": {
        "contextId": context_id,
        "data": context_data
    }
})

3. Resource Access

Agents can access resources from external systems:

# Request a resource from external system
resource_response = await agent.mcp_client.send_request({
    "method": "getResource",
    "params": {
        "uri": "documentation://api/reference",
        "contextId": context_id
    }
})

# Use the resource in the agent
documentation = resource_response.content

Practical Example: Code Review Collaboration

Here's how AgentNexus agents could collaborate with external systems for code review:

async def collaborative_code_review(code_content, language):
    """Perform a collaborative code review using internal and external agents"""
    
    # Create shared context
    context_id = f"code-review-{uuid.uuid4()}"
    context_data = {
        "code": {
            "language": language,
            "content": code_content
        }
    }
    
    # Initialize our code analysis agent
    analysis_agent = await initialize_code_analysis_agent()
    
    # Connect to external security scanning service
    security_client = await McpClient.connect("https://security-scanner.example.com")
    await security_client.initialize()
    
    # Share context with security scanner
    await security_client.send_request({
        "method": "createContext",
        "params": {
            "contextId": context_id,
            "data": context_data
        }
    })
    
    # Request security analysis in parallel with our own analysis
    security_task = asyncio.create_task(
        security_client.send_request({
            "method": "analyzeSecurity",
            "params": {
                "contextId": context_id,
                "options": {
                    "depth": "thorough",
                    "includeRemediation": True
                }
            }
        })
    )
    
    # Perform our own code quality analysis
    quality_analysis = await analysis_agent.analyze_code_quality(
        code_content, 
        language
    )
    
    # Wait for security analysis to complete
    security_analysis = await security_task
    
    # Combine results
    combined_review = {
        "quality": quality_analysis,
        "security": security_analysis,
        "combined_recommendations": merge_recommendations(
            quality_analysis.get("recommendations", []),
            security_analysis.get("recommendations", [])
        )
    }
    
    return combined_review

Enhancing AgentNexus with MCP Support

To implement MCP in AgentNexus, you'd need to add these components:

1. MCP Protocol Implementation

Create core MCP protocol classes:

# agentnexus/mcp/protocol.py
from jsonrpclib import Server
import asyncio
import json

class McpProtocol:
    """Base implementation of MCP protocol handling"""
    
    def __init__(self):
        self.request_handlers = {}
        self.notification_handlers = {}
        self.next_id = 1
    
    async def handle_message(self, message_data):
        """Process an incoming MCP message"""
        message = json.loads(message_data)
        
        if "method" in message:
            if "id" in message:
                # This is a request
                return await self._handle_request(message)
            else:
                # This is a notification
                return await self._handle_notification(message)
        elif "id" in message:
            # This is a response
            return await self._handle_response(message)
    
    # Additional protocol methods...

2. Transport Layer Implementation

Support different MCP transport mechanisms:

# agentnexus/mcp/transport.py
import asyncio
import aiohttp
from aiohttp import web
from typing import Callable, Awaitable, Dict, Any

class StdioTransport:
    """MCP transport using standard input/output"""
    
    def __init__(self):
        self.message_handler = None
    
    async def connect(self, message_handler: Callable[[str], Awaitable[None]]):
        """Connect the transport with a message handler"""
        self.message_handler = message_handler
        
        # Set up stdin reader
        loop = asyncio.get_event_loop()
        reader = asyncio.StreamReader()
        protocol = asyncio.StreamReaderProtocol(reader)
        await loop.connect_read_pipe(lambda: protocol, sys.stdin)
        
        # Start reading messages
        asyncio.create_task(self._read_messages(reader))
    
    async def _read_messages(self, reader):
        """Read messages from stdin"""
        while True:
            line = await reader.readline()
            if not line:
                break
                
            if self.message_handler:
                await self.message_handler(line.decode('utf-8'))
    
    async def send_message(self, message: str):
        """Send a message over stdout"""
        print(message, flush=True)

class HttpSseTransport:
    """MCP transport using HTTP and Server-Sent Events"""
    
    # Implementation for HTTP+SSE transport...

3. Adapter Layer for Existing Agents

Create adapter classes to make existing agents MCP-compatible:

# agentnexus/mcp/adapters.py
from agentnexus.base_types import AgentConfig, ActionType
from agentnexus.mcp.protocol import McpProtocol
from agentnexus.mcp.transport import StdioTransport, HttpSseTransport

class McpAgentAdapter:
    """Adapter to expose an AgentNexus agent as an MCP server"""
    
    def __init__(self, agent_config: AgentConfig):
        self.agent_config = agent_config
        self.protocol = McpProtocol()
        self.transport = None
        
        # Map agent actions to MCP methods
        self._map_actions_to_methods()
    
    def _map_actions_to_methods(self):
        """Register agent actions as MCP methods"""
        for action in self.agent_config.actions:
            self.protocol.register_method(
                action.name,
                lambda params, action=action: self._execute_action(action, params)
            )
    
    async def _execute_action(self, action, params):
        """Execute an agent action when requested via MCP"""
        # Convert MCP params to action input
        input_data = self._convert_to_action_input(params, action)
        
        # Execute the action
        result = await action.handler(input_data)
        
        # Return the result
        return result
    
    async def start(self, transport_type="stdio"):
        """Start the MCP server with the specified transport"""
        if transport_type == "stdio":
            self.transport = StdioTransport()
        elif transport_type == "http":
            self.transport = HttpSseTransport()
        else:
            raise ValueError(f"Unsupported transport type: {transport_type}")
            
        await self.transport.connect(self.protocol.handle_message)

Benefits for AgentNexus

Implementing MCP clients/servers in AgentNexus would offer several key advantages:

External Integration: Connect with specialized tools, services, and agents outside your system
Standardized Communication: Use a common protocol recognized across the LLM ecosystem
Enhanced Capabilities: Access specialized capabilities from external systems when needed
Flexible Deployment: Support multiple deployment patterns (local, remote, distributed)
Context Preservation: Maintain shared understanding across different systems
Progressive Enhancement: Start with basic MCP support and expand as needed

Conclusion

By implementing MCP client/server support in AgentNexus, you would create a framework that not only manages internal agent interactions but can also participate in broader ecosystems of LLM-powered applications. This approach provides immediate value by enabling communication with external specialized systems while paving the way for more advanced multi-agent collaborations in the future.

The most practical first step would be to implement basic MCP client capabilities in AgentNexus, allowing your agents to connect to external MCP servers and leverage their specialized capabilities. From there, you could progressively add server capabilities to expose your agents' functionality to other systems.

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