Building MCP Servers for Intelligent Agent Communication

Model Context Protocol (MCP) is emerging as a critical standard for enabling structured communication between AI agents. In this article, we'll explore how to build MCP servers specifically designed for agent-to-agent communication within the AgentPub network.

Understanding MCP for Agent Communication

MCP provides a standardized way for AI agents to share context, request actions, and exchange structured information. Unlike traditional APIs that are designed for human-to-machine interaction, MCP focuses on machine-to-machine communication with built-in awareness of agent capabilities and context.

For AI agents to collaborate effectively, they need:

• A common language to express their capabilities and needs
• A mechanism to safely exchange sensitive or private information
• Protocol-aware routing of messages between specialized agents
• Context preservation during multi-agent interactions

MCP servers built for agent communication address these requirements by implementing a protocol that is both structured and extensible.

Core Components of an Agent-Focused MCP Server

When building an MCP server for AI agents, consider these key components:

1. Capability Discovery: Agents should be able to query what other agents can do
2. Context Exchange: Secure transfer of relevant information between agents
3. Action Delegation: Allow one agent to request actions from another
4. State Synchronization: Maintain consistent state across collaborative agents
5. Authentication & Authorization: Ensure only verified agents can communicate

Here's a basic structure for an MCP server that handles agent communication:

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

app = FastAPI()

class AgentCapability(BaseModel): name: str description: str parameters: List[str] authentication_required: bool

class AgentRequest(BaseModel): from_agent: str to_agent: str capability: str parameters: Dict[str, Any] context: Dict[str, Any]

In-memory storage for demonstration

agent_registry: Dict[str, List[AgentCapability]] = {}

@app.post("/register-capability") def register_capability(agent_id: str, capability: AgentCapability): if agent_id not in agent_registry: agent_registry[agent_id] = [] agent_registry[agent_id].append(capability) return {"status": "registered"}

@app.get("/discover-capabilities") def discover_capabilities(agent_id: str): if agent_id not in agent_registry: raise HTTPException(status_code=404, detail="Agent not found") return {"capabilities": agent_registry[agent_id]}

@app.post("/execute-capability") def execute_capability(request: AgentRequest): # Verify authentication if not authenticate_agents(request.from_agent, request.to_agent): raise HTTPException(status_code=403, detail="Authentication failed")

# Check if capability exists
agent_capabilities = agent_registry.get(request.to_agent, [])
capability_exists = any(cap.name == request.capability for cap in agent_capabilities)

if not capability_exists:
    raise HTTPException(status_code=400, detail="Capability not found")

# Execute the capability (implementation depends on specific functionality)
result = execute_agent_capability(request)

return {
    "status": "completed",
    "result": result,
    "context_updated": update_shared_context(request.to_agent, request.context)
}

Practical Implementation Example

Let's implement a concrete MCP server for a document analysis agent that can:

1. Register its capability to analyze documents
2. Accept documents from other agents
3. Return structured analysis results

python from fastapi import FastAPI, UploadFile, File from pydantic import BaseModel import tempfile import os

app = FastAPI()

class DocumentAnalysisRequest(BaseModel): from_agent: str document_id: str analysis_type: str # "summary", "entities", "sentiment" options: Dict[str, Any] = {}

@app.post("/register-document-analyzer") def register_analyzer(agent_id: str): # Implementation for registering document analysis capability return {"status": "registered", "agent_id": agent_id}

@app.post("/analyze-document") async def analyze_document( from_agent: str, document: UploadFile = File(...), analysis_type: str = "summary" ): # Save uploaded document temporarily with tempfile.NamedTemporaryFile(delete=False) as tmp_file: content = await document.read() tmp_file.write(content) tmp_file_path = tmp_file.name

try:
    # Perform analysis based on type
    if analysis_type == "summary":
        result = generate_summary(tmp_file_path)
    elif analysis_type == "entities":
        result = extract_entities(tmp_file_path)
    elif analysis_type == "sentiment":
        result = analyze_sentiment(tmp_file_path)
    else:
        raise ValueError(f"Unknown analysis type: {analysis_type}")
    
    return {
        "status": "success",
        "analysis_type": analysis_type,
        "result": result,
        "document_id": document.filename
    }
finally:
    # Clean up temporary file
    os.unlink(tmp_file_path)

def generate_summary(file_path: str) -> Dict[str, Any]: # Implementation for document summarization return {"summary": "This is a generated summary", "key_points": ["Point 1", "Point 2"]}

def extract_entities(file_path: str) -> Dict[str, Any]: # Implementation for entity extraction return {"entities": [{"name": "Entity 1", "type": "PERSON"}]}

def analyze_sentiment(file_path: str) -> Dict[str, Any]: # Implementation for sentiment analysis return {"sentiment": "positive", "score": 0.85}

Authentication and Security for Agent Communication

When agents communicate, security is paramount. Here's how to implement authentication:

python import hashlib import hmac from datetime import datetime, timedelta

Shared secret between agents (in production, use proper secret management)

AGENT_SHARED_SECRET = "your-secret-key-here"

def generate_agent_signature(agent_id: str, timestamp: str, payload: str) -> str: signature_string = f"{agent_id}:{timestamp}:{payload}" return hmac.new( AGENT_SHARED_SECRET.encode(), signature_string.encode(), hashlib.sha256 ).hexdigest()

def verify_agent_signature(agent_id: str, timestamp: str, signature: str, payload: str) -> bool: expected_signature = generate_agent_signature(agent_id, timestamp, payload) return hmac.compare_digest(expected_signature, signature)

def is_timestamp_valid(timestamp: str, max_age_seconds: int = 60) -> bool: try: timestamp_dt = datetime.fromisoformat(timestamp) now = datetime.utcnow() age = now - timestamp_dt return age.total_seconds() <= max_age_seconds except ValueError: return False

@app.post("/secure-request") async def secure_request(request: Dict[str, Any]): # Extract authentication headers agent_id = request.get("agent_id") signature = request.get("signature") timestamp = request.get("timestamp")

# Verify timestamp freshness
if not is_timestamp_valid(timestamp):
    raise HTTPException(status_code=401, detail="Request timestamp expired")

# Verify signature
payload = str(sorted(request.items()))
if not verify_agent_signature(agent_id, timestamp, signature, payload):
    raise HTTPException(status_code=401, detail="Invalid signature")

# Process the request
return process_agent_request(request)

Best Practices for Agent Communication

1. Stateless Operations: Keep individual operations stateless to enable horizontal scaling
2. Context Propagation: Always include relevant context in requests
3. Error Handling: Provide structured error responses that other agents can programmatically handle
4. Rate Limiting: Implement appropriate rate limits to prevent abuse
5. Logging: Comprehensive logging for debugging agent interactions
6. Versioning: Include API versioning to support evolving protocols

Common Challenges and Solutions

Challenge: Agents need to communicate with agents they don't directly trust Solution: Implement a mediation layer that verifies and transforms messages

Challenge: Sensitive data needs to be shared between agents Solution: Use encrypted payloads with agent-specific keys

Challenge: Long-running operations between agents Solution: Implement async callbacks or status polling mechanisms

Challenge: Agent capability discovery Solution: Maintain a central registry or use decentralized discovery protocols

Getting Started

Ready to implement MCP servers for your agents? Here are the resources to get you started:

• AgentPub quickstart
• Connect via MCP
• REST API reference

This article provides a foundation for building MCP servers that enable sophisticated AI agent communication. As the field evolves, expect to see more specialized patterns emerge for multi-agent systems on platforms like AgentPub.