MCP for Agentic AI

Build AI Agents That Understand Poultry

PoultrySync’s Model Context Protocol (MCP) integration enables developers to create AI agents that understand poultry operations and can access farm data intelligently.

What is MCP?

Model Context Protocol is an open standard that allows AI models (like Claude, GPT-4, Gemini) to access external data and systems safely and securely. It’s like giving AI agents “tools” they can use to interact with the real world.

Why MCP for Poultry?

Traditional AI Limitations:

Generic responses without farm context
Can’t access real-time data
No ability to take actions
Hallucinates about farm specifics

MCP-Enabled AI Agents:

Understand your specific operation
Access live farm data
Execute actions safely
Provide accurate, contextual insights

Use Cases

Farm Management Assistant

Agent Capabilities:

Answer questions about current flocks
Analyze performance trends
Generate custom reports
Predict outcomes based on data
Recommend optimizations

Example Conversation:

User: "How are my flocks performing this week?"

AI Agent (via MCP):
- Queries PoultrySync API for active flocks
- Analyzes current performance vs. targets
- Checks for any alerts or issues
- Generates summary with recommendations

Response: "You have 12 active flocks. Overall FCR is 1.67
(on target). House B3 has elevated mortality (0.15% yesterday)
- I recommend a vet visit. Houses A1-A4 are trending above
target weight and should harvest 2 days early for optimal value."

Veterinary Consultant

Agent Capabilities:

Review flock health data
Analyze symptom patterns
Suggest differential diagnoses
Recommend treatment protocols
Track treatment outcomes

Example:

User: "House 5 has respiratory symptoms"

AI Agent:
- Retrieves House 5 flock data
- Analyzes recent mortality trends
- Checks environmental data
- Reviews similar past cases
- Consults disease database

Response: "Based on the data, possible causes are Newcastle
Disease, Infectious Bronchitis, or Mycoplasma. Current
temperature variations (18-26°C daily swing) may be
contributing. Recommend: 1) Vet visit for diagnosis,
2) Stabilize temperature at 22-23°C, 3) Separate affected
birds. I've created a task for your vet and logged this issue."

Financial Analyst

Agent Capabilities:

Real-time profitability analysis
Cost optimization suggestions
Scenario modeling
Budget tracking
ROI calculations

Example:

User: "What if feed costs increase 10%?"

AI Agent:
- Retrieves current cost structure
- Models 10% feed cost increase
- Calculates impact on margins
- Suggests mitigation strategies

Response: "A 10% feed increase would reduce margin by $0.12/kg
(18% profit reduction). To offset: Option 1: Improve FCR from
1.67 to 1.60 through better management. Option 2: Negotiate
2.5% higher contract price. Option 3: Reduce cycle length by
2 days. I can model each scenario in detail if you'd like."

MCP Servers Provided

PoultrySync Core Server

Capabilities:

Farm and house data access
Flock performance queries
Feed and inventory information
Health and mortality records
Financial data (with permissions)

Tools Available:

{
  "get_flock_performance": "Retrieve current performance metrics",
  "get_daily_data": "Access daily production data",
  "analyze_trends": "Analyze performance over time",
  "get_alerts": "Retrieve active alerts and issues",
  "get_forecast": "AI-powered performance predictions"
}

Poultry Knowledge Base Server

Capabilities:

Disease information database
Best practice library
Feed formulation guidance
Breed characteristics
Regulatory compliance info

Tools Available:

{
  "disease_lookup": "Get disease information",
  "treatment_protocol": "Retrieve treatment protocols",
  "best_practice": "Access industry best practices",
  "breed_info": "Get breed specifications",
  "compliance_check": "Check regulatory requirements"
}

Action Server

Capabilities:

Create tasks and reminders
Schedule activities
Send notifications
Generate reports
Update records (with permissions)

Tools Available:

{
  "create_task": "Create task for team member",
  "schedule_visit": "Schedule farm visit",
  "send_alert": "Send notification to user/team",
  "generate_report": "Create custom report",
  "update_record": "Modify farm data (restricted)"
}

Getting Started

1. Install MCP Client

For Claude Desktop:

// claude_desktop_config.json
{
  "mcpServers": {
    "poultrysync": {
      "command": "npx",
      "args": ["-y", "@poultrysync/mcp-server"],
      "env": {
        "POULTRYSYNC_API_KEY": "your_api_key_here"
      }
    }
  }
}

For Custom Applications:

npm install @modelcontextprotocol/sdk @poultrysync/mcp-server

2. Configure Access

import { MCPServer } from '@poultrysync/mcp-server';

const server = new MCPServer({
  apiKey: process.env.POULTRYSYNC_API_KEY,
  farmIds: ['farm_123', 'farm_456'], // Optional: restrict to specific farms
  permissions: {
    read: true,
    write: false,  // Recommend read-only for most agents
    admin: false
  }
});

3. Create AI Agent

from anthropic import Anthropic
import mcp

client = Anthropic(api_key="your_anthropic_key")

# Connect to PoultrySync MCP server
mcp_client = mcp.Client("poultrysync")

# Create agent with PoultrySync tools
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=4096,
    tools=mcp_client.get_tools(),
    messages=[{
        "role": "user",
        "content": "What's the FCR trend for Farm 123 this month?"
    }]
)

# Agent will use MCP tools to query PoultrySync and respond

Advanced Examples

Custom Farm Monitoring Agent

// autonomous-monitor.js
import { MCPServer } from '@poultrysync/mcp-server';
import { Anthropic } from '@anthropic-ai/sdk';

class FarmMonitorAgent {
  constructor(farmIds) {
    this.mcp = new MCPServer({
      apiKey: process.env.POULTRYSYNC_API_KEY,
      farmIds
    });
    this.ai = new Anthropic({
      apiKey: process.env.ANTHROPIC_API_KEY
    });
  }

  async dailyCheckup() {
    const prompt = `
      Analyze all active flocks and identify any issues requiring attention.
      For each issue:
      1. Assess severity
      2. Recommend action
      3. Create task if needed

      Provide a summary email for the farm manager.
    `;

    const response = await this.ai.messages.create({
      model: 'claude-3-5-sonnet-20241022',
      max_tokens: 8000,
      tools: this.mcp.getTools(),
      messages: [{ role: 'user', content: prompt }]
    });

    return response.content;
  }
}

// Run daily at 6 AM
const agent = new FarmMonitorAgent(['farm_123']);
setInterval(() => agent.dailyCheckup(), 24 * 60 * 60 * 1000);

Multi-Agent System

# multi_agent_system.py

class AnalystAgent:
    """Analyzes farm performance data"""
    role = "performance_analyst"

class VetAgent:
    """Focuses on health and disease"""
    role = "veterinarian"

class FinanceAgent:
    """Handles cost and profitability"""
    role = "financial_analyst"

class CoordinatorAgent:
    """Routes questions to appropriate specialist"""

    def route_question(self, question):
        # Use AI to determine which specialist to consult
        if "health" in question or "disease" in question:
            return VetAgent()
        elif "cost" in question or "profit" in question:
            return FinanceAgent()
        else:
            return AnalystAgent()

Security & Permissions

API Key Scoping

Create separate API keys for different agents with appropriate permissions:

// Read-only agent for reporting
const reportAgent = {
  apiKey: "pk_readonly_...",
  permissions: ["read:farms", "read:flocks", "read:performance"]
};

// Action-enabled agent for task management
const taskAgent = {
  apiKey: "pk_actions_...",
  permissions: ["read:farms", "write:tasks", "write:notifications"]
};

Audit Logging

All MCP tool calls are logged:

{
  "timestamp": "2025-01-15T10:30:00Z",
  "agent_id": "agent_farm123_monitor",
  "tool": "get_flock_performance",
  "params": {"flock_id": "flock_456"},
  "user": "system",
  "result": "success"
}

Rate Limiting

MCP servers respect standard API rate limits. Implement client-side caching for frequently accessed data.

Best Practices

1. Provide Context

Give agents clear instructions about their role:

You are a poultry farm management assistant with expertise in
broiler production. You have access to real-time farm data and
should provide practical, actionable advice based on industry
best practices and the specific farm's historical performance.

2. Validate AI Decisions

For critical actions, require human approval:

if (action.type === 'medication_order' || action.cost > 1000) {
  await requestHumanApproval(action);
}

3. Monitor Agent Behavior

Track what tools agents are using and how often:

analytics.track('mcp_tool_usage', {
  tool: 'get_flock_performance',
  frequency: 'high',
  agent: 'daily_monitor'
});

4. Graceful Degradation

Handle API failures gracefully:

try:
    data = mcp.get_flock_performance(flock_id)
except MCPError:
    return "I'm unable to access farm data right now. Please try again later."

Resources

Documentation

Tutorials

Building Your First Farm AI Agent
Multi-Agent Farm Management System
Voice-Activated Farm Assistant
Slack Bot Integration

Community

MCP Forum
Discord Channel
Monthly Developer Office Hours

Pricing

MCP Access

Included: Professional and Enterprise tiers
API Costs: Standard API rate limits apply
No Additional Fees: Use any MCP-compatible AI model

Recommended AI Models

Anthropic Claude: Best for complex reasoning
OpenAI GPT-4: Excellent general capabilities
Google Gemini: Good multilingual support
Self-hosted models: Llama, Mistral (privacy-focused)

Get Started with MCP | View Examples

Related Tools:

APIs - REST and GraphQL APIs
Workflow Builder - Visual automation
AI Assistant - Built-in AI features

MCP for Agentic AI Build Intelligent Poultry Assistants

MCP for Agentic AI

Build AI Agents That Understand Poultry

What is MCP?

Why MCP for Poultry?

Use Cases

Farm Management Assistant

Veterinary Consultant

Financial Analyst

MCP Servers Provided

PoultrySync Core Server

Poultry Knowledge Base Server

Action Server

Getting Started

1. Install MCP Client

2. Configure Access

3. Create AI Agent

Advanced Examples

Custom Farm Monitoring Agent

Multi-Agent System

Security & Permissions

API Key Scoping

Audit Logging

Rate Limiting

Best Practices

1. Provide Context

2. Validate AI Decisions

3. Monitor Agent Behavior

4. Graceful Degradation

Resources

Documentation

Tutorials

Community

Pricing

MCP Access

Recommended AI Models