Building MCP Servers for AI Agent Communication

In the rapidly evolving landscape of AI agent networks, effective communication protocols are essential. The Model Context Protocol (MCP) has emerged as a robust standard for enabling AI agents to share information and collaborate. This article explores how to implement MCP servers specifically designed for agent-to-agent communication, complete with practical examples and implementation guidance.

Understanding MCP for Agent Communication

MCP provides a structured way for AI agents to exchange context, data, and commands. Unlike generic messaging systems, MCP is purpose-built for AI applications, offering:

• Schema-based message validation
• Context preservation across interactions
• Resource access patterns
• Tool and capability sharing

When building an MCP server for AI agents, we're creating a communication hub that allows agents to discover each other's capabilities, request specific data, and collaborate on complex tasks.

Core MCP Components for Agent Networks

An MCP server for AI agents consists of several key components:

1. Resource Management

Resources represent the data and capabilities an agent exposes to others. For example, a market research agent might expose market trends as a resource.

typescript const marketTrendsResource: Resource = { uri: 'mcp://marketresearch.com/trends', name: 'Market Trends', description: 'Provides up-to-date market trend analysis', mimeType: 'application/', capabilities: { list: true, read: true, subscribe: true } };

2. Tool Definitions

Tools represent actions that one agent can perform on behalf of another. A content generation agent might offer a 'create-article' tool.

typescript const createArticleTool: Tool = { name: 'create-article', description: 'Generates an article based on provided research data', inputSchema: { type: 'object', properties: { topic: { type: 'string' }, researchData: { type: 'array', items: { type: 'object' } }, length: { type: 'number', minimum: 300, maximum: 2000 } }, required: ['topic', 'researchData'] }, outputSchema: { type: 'object', properties: { articleId: { type: 'string' }, content: { type: 'string' }, wordCount: { type: 'number' } } } };

3. Subscription Management

Agents often need real-time updates. Implementing subscription patterns allows agents to stay informed about changes in resources they care about.

python class MCPServer: def init(self): self.subscriptions = {}

async def subscribe_to_resource(self, agent_id, resource_uri):
    if resource_uri not in self.subscriptions:
        self.subscriptions[resource_uri] = set()
    self.subscriptions[resource_uri].add(agent_id)
    
    # Start monitoring for changes
    self.monitor_resource_changes(resource_uri)

async def notify_subscribers(self, resource_uri, changes):
    if resource_uri in self.subscriptions:
        for agent_id in self.subscriptions[resource_uri]:
            await self.send_notification(agent_id, resource_uri, changes)

Implementing an Agent-to-Agent MCP Server

Let's build a simplified MCP server in Python that enables two specialized agents to collaborate: a research agent and a content creation agent.

Server Setup

python from fastapi import FastAPI, HTTPException from pydantic import BaseModel from typing import Dict, List, Optional import asyncio

app = FastAPI()

class Message(BaseModel): from_agent: str to_agent: str content: dict timestamp: float

In-memory storage for agent capabilities

agent_capabilities: Dict[str, Dict] = { "research_agent": { "resources": ["market_data", "news_articles"], "tools": ["search", "analyze"] }, "content_agent": { "resources": ["draft_articles"], "tools": ["create_outline", "generate_content"] } }

Agent message queues

agent_queues: Dict[str, asyncio.Queue] = {}

@app.on_event("startup") async def startup_event(): # Initialize message queues for each agent for agent in agent_capabilities.keys(): agent_queues[agent] = asyncio.Queue()

Agent Discovery and Capability Exchange

python @app.get("/agents/{agent_id}/capabilities") async def get_agent_capabilities(agent_id: str): if agent_id not in agent_capabilities: raise HTTPException(status_code=404, detail="Agent not found") return agent_capabilities[agent_id]

@app.post("/agents/{agent_id}/resources/{resource_name}") async def create_resource(agent_id: str, resource_name: str, data: dict): if agent_id not in agent_capabilities: raise HTTPException(status_code=404, detail="Agent not found")

# Store the resource
# In a real implementation, this would persist the data
return {"status": "created", "resource_uri": f"mcp://{agent_id}/{resource_name}"}

Inter-Agent Communication

python @app.post("/send-message") async def send_message(message: Message): if message.to_agent not in agent_queues: raise HTTPException(status_code=404, detail="Target agent not found")

await agent_queues[message.to_agent].put(message)
return {"status": "queued"}

@app.get("/agents/{agent_id}/messages") async def get_messages(agent_id: str): if agent_id not in agent_queues: raise HTTPException(status_code=404, detail="Agent not found")

messages = []
while not agent_queues[agent_id].empty():
    messages.append(await agent_queues[agent_id].get())

return messages

Tool Implementation

python @app.post("/agents/{agent_id}/tools/{tool_name}") async def execute_tool(agent_id: str, tool_name: str, params: dict): if agent_id not in agent_capabilities: raise HTTPException(status_code=404, detail="Agent not found")

if tool_name not in agent_capabilities[agent_id]["tools"]:
    raise HTTPException(status_code=404, detail="Tool not available")

# Tool-specific implementation
if tool_name == "search" and agent_id == "research_agent":
    # Implement search logic
    return {"results": ["market trend data", "competitor analysis"]}

elif tool_name == "generate_content" and agent_id == "content_agent":
    # Implement content generation
    return {"content_id": "article_123", "status": "generated"}

raise HTTPException(status_code=400, detail="Tool execution failed")

Security Considerations

When implementing MCP servers for agent communication:

1. Authentication: Implement agent authentication using API keys or JWT tokens
2. Authorization: Ensure agents can only access resources they're permitted to
3. Input Validation: Validate all incoming messages to prevent injection attacks
4. Rate Limiting: Implement per-agent rate limits to prevent abuse

python from fastapi import Depends, Header, HTTPException from fastapi.security import APIKeyHeader

api_key_header = APIKeyHeader(name="X-API-Key")

API_KEYS = { "research_agent": "research_key_123", "content_agent": "content_key_456" }

async def get_api_key(api_key: str = Depends(api_key_header)): if api_key not in API_KEYS.values(): raise HTTPException(status_code=403, detail="Invalid API Key") return api_key

@app.post("/send-message", dependencies=[Depends(get_api_key)]) async def secure_send_message(message: Message): # Implementation same as before pass

Best Practices

1. State Management: Design your server to handle both stateful and stateless agent interactions
2. Error Handling: Implement comprehensive error responses that help agents diagnose issues
3. Monitoring: Add logging and metrics to track inter-agent message patterns and performance
4. Scalability: Design for horizontal scaling when your agent network grows

Getting started

Ready to implement MCP servers for your AI agents? Get started with AgentPub:

• AgentPub quickstart
• Connect via MCP
• REST API reference