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Guide

Building AEO Agents on Top of SearchFit MCP

Learn to build custom Answer Engine Optimization agents using SearchFit's MCP tools. Step-by-step guide for AI engineers integrating AEO tracking into LLM workflows.

KKevin14 minutes read

building aeo agents on Top of SearchFit MCP

Why AEO Agents Matter Now

Your competitors are already getting cited in ChatGPT, Perplexity, and Gemini. Your brand visibility in AI search isn't just declining, it's invisible to your team. While you're optimizing for Google's blue links, answer engines are reshaping how buyers discover solutions, and most SaaS and e-commerce teams have zero visibility into whether their content is being recommended by Claude, Grok, or Gemini.

This is where custom AEO agents come in. Instead of manually checking rankings across six different AI platforms each week, you can build an intelligent agent that continuously monitors your brand visibility, detects citation gaps, and recommends content optimizations, all integrated into your existing AI workflows.

SearchFit's Model Context Protocol (MCP) tools let you embed answer engine optimization directly into your AI agents. Whether you're building a custom agent in Claude, composing workflows in your LLM framework, or integrating real-time AEO data into your content strategy platform, this guide walks you through the entire process.

Prerequisites: What You Need Before Starting

Before you begin building AEO agents on top of SearchFit MCP, ensure you have the following in place:

Technical Requirements:

  • A working knowledge of Python, Node.js, or another language supported by your chosen MCP framework
  • Access to SearchFit's MCP server and valid API credentials
  • A local development environment with Git, Docker (optional but recommended), and your preferred code editor
  • Familiarity with how Model Context Protocol (MCP) works and basic agent architecture
  • Understanding of JSON and REST API fundamentals

SearchFit Access:

  • An active SearchFit account with access to AI tracking capabilities across ChatGPT, Perplexity, Gemini, Claude, Grok, and Google
  • API keys or authentication tokens from your SearchFit dashboard
  • At least one tracked domain or brand configured in your SearchFit workspace
  • Understanding of your AEO goals (citation tracking, visibility monitoring, content recommendations)

Agent Framework Choice: You'll need to select an MCP-compatible framework. Popular options include:

  • Claude (via Anthropic's SDK)
  • LangChain with MCP integration
  • Spring AI (with MCP support)
  • Custom agent frameworks using the MCP specification

Familiarity with your chosen framework's documentation is essential before proceeding.

Understanding SearchFit's MCP Architecture

SearchFit's MCP tools expose your AI search visibility data as callable functions that agents can invoke. Rather than building custom API wrappers, MCP provides a standardized protocol that lets your agent discover, understand, and execute SearchFit operations automatically.

The SearchFit MCP server exposes several key tool categories:

Visibility Tracking Tools retrieve your current brand rankings and citation rates across all monitored AI engines. These tools answer questions like "Is my domain cited in ChatGPT for SaaS pricing queries?" or "What's my Perplexity visibility trend over the last 30 days?"

Content Analysis Tools examine your existing content against AEO best practices and flag optimization opportunities. They identify gaps between what you've published and what answer engines are actually recommending to users.

Competitor Intelligence Tools benchmark your AEO performance against competitors, showing which brands are getting cited more frequently and why.

Citation Tracking Tools monitor which specific pieces of content are being cited by which engines, allowing you to understand which content types and topics resonate with LLMs.

Understanding this architecture is critical because it shapes how you'll design your agent's decision logic. An agent that only checks visibility metrics is fundamentally different from one that also analyzes content gaps and recommends rewrites.

Step 1: Set Up Your MCP Server Connection

The first step is establishing a secure, authenticated connection between your agent and SearchFit's MCP server. This involves configuring your environment, installing dependencies, and testing the connection.

Install Required Dependencies:

Start by creating a new project directory and initializing your environment:

mkdir aeo-agent && cd aeo-agent
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install anthropic mcp

If you're using Node.js instead:

npm init -y
npm install @anthropic-ai/sdk mcp

Configure Authentication:

Create a .env file in your project root with your SearchFit credentials:

SEARCHFIT_API_KEY=your_searchfit_api_key_here
SEARCHFIT_MCP_SERVER_URL=https://api.searchfit.ai/mcp
YOUR_DOMAIN=yourdomain.com

Never commit this file to version control. Use a secrets manager like AWS Secrets Manager, HashiCorp Vault, or your platform's built-in secret storage for production deployments.

Initialize the MCP Connection:

Create a file called searchfit_mcp.py (Python) or searchfit-mcp.js (Node.js) to handle the MCP connection:

import os
from anthropic import Anthropic
from mcp import MCPClient

class SearchFitMCPClient:
def __init__(self):
self.api_key = os.getenv('SEARCHFIT_API_KEY')
self.mcp_url = os.getenv('SEARCHFIT_MCP_SERVER_URL')
self.domain = os.getenv('YOUR_DOMAIN')
self.client = MCPClient()
self.tools = []

async def connect(self):
"""Establish connection to SearchFit MCP server"""
await self.client.connect_server(
url=self.mcp_url,
auth_token=self.api_key
)
self.tools = await self.client.discover_tools()
print(f"Connected to SearchFit MCP. Found {len(self.tools)} available tools.")

async def list_available_tools(self):
"""Display all available SearchFit MCP tools"""
for tool in self.tools:
print(f"- {tool.name}: {tool.description}")

Test the Connection:

Write a simple test script to verify your connection works:

import asyncio
from searchfit_mcp import SearchFitMCPClient

async def test_connection():
client = SearchFitMCPClient()
await client.connect()
await client.list_available_tools()

if __name__ == "__main__":
asyncio.run(test_connection())

Run this and verify you see a list of available tools. If you encounter authentication errors, double-check your API key and MCP server URL. For detailed guidance on MCP authorization and permissions, refer to best practices around MCP authorization using OAuth 2.1 and scopes to ensure your agent has appropriate least-privilege access.

Step 2: Define Your Agent's Purpose and Tool Selection

Before writing agent logic, clarify what your AEO agent should actually do. Different use cases require different tool combinations.

Define Your Agent's Primary Goal:

Ask yourself:

  • Is this agent monitoring visibility and alerting you to drops?
  • Is it analyzing content gaps and recommending rewrites?
  • Is it competitive benchmarking?
  • Is it a real-time chatbot that answers questions about your AEO performance?

Each goal shapes which SearchFit MCP tools your agent will invoke and how frequently.

Map Tools to Workflows:

Let's say your goal is: "Alert the content team when our brand visibility drops below 30% in any major AI engine, and recommend content optimizations to recover."

Your agent would need:

  1. Visibility Tracking Tool - Check current citation rates across all engines
  2. Threshold Detection Logic - Identify drops below 30%
  3. Content Analysis Tool - Find which content types are underperforming
  4. Recommendation Engine - Suggest specific rewrites or new content
  5. Alert/Notification Tool - Notify your team via Slack, email, or your platform

Select and Document Your Tools:

Create a tools_manifest.json file listing which SearchFit MCP tools your agent will use:

{
"agent_name": "AEO Visibility Monitor",
"purpose": "Monitor brand visibility across AI engines and alert on drops",
"tools_required": [
{
"name": "get_visibility_metrics",
"description": "Retrieve current visibility scores across ChatGPT, Perplexity, Gemini, Claude, Grok",
"frequency": "daily",
"critical": true
},
{
"name": "get_citation_trends",
"description": "Get 30-day visibility trends to detect drops",
"frequency": "daily",
"critical": true
},
{
"name": "analyze_content_gaps",
"description": "Identify content types and topics with low citation rates",
"frequency": "weekly",
"critical": false
},
{
"name": "get_competitor_benchmarks",
"description": "Compare visibility against competitors",
"frequency": "weekly",
"critical": false
}
]
}

This manifest becomes your agent's blueprint and helps you prioritize which tools to implement first.

Step 3: Build Your Agent's Core Logic

Now you'll create the actual agent that consumes SearchFit MCP tools. This is where your agent becomes intelligent, it's not just calling tools, it's reasoning about the data and making decisions.

Create the Agent Class:

import asyncio
import json
from datetime import datetime
from anthropic import Anthropic
from searchfit_mcp import SearchFitMCPClient

class AEOAgent:
def __init__(self, domain: str):
self.domain = domain
self.mcp_client = SearchFitMCPClient()
self.anthropic_client = Anthropic()
self.conversation_history = []
self.visibility_threshold = 0.30  # 30% minimum visibility

async def initialize(self):
"""Initialize MCP connection and load available tools"""
await self.mcp_client.connect()
print(f"AEO Agent initialized for domain: {self.domain}")

async def check_visibility(self) -> dict:
"""Call SearchFit MCP to get current visibility metrics"""
result = await self.mcp_client.call_tool(
tool_name="get_visibility_metrics",
params={"domain": self.domain}
)
return result

async def analyze_performance(self) -> dict:
"""Get 30-day trends to detect visibility changes"""
result = await self.mcp_client.call_tool(
tool_name="get_citation_trends",
params={
"domain": self.domain,
"days": 30
}
)
return result

async def identify_content_gaps(self) -> dict:
"""Analyze which content types have low citation rates"""
result = await self.mcp_client.call_tool(
tool_name="analyze_content_gaps",
params={"domain": self.domain}
)
return result

def _format_visibility_data(self, visibility: dict) -> str:
"""Format visibility metrics for Claude's analysis"""
formatted = "Current AI Search Visibility:\n"
for engine, metrics in visibility.items():
formatted += f"\n{engine}:\n"
formatted += f"  Citation Rate: {metrics['citation_rate']:.1%}\n"
formatted += f"  Search Volume: {metrics['search_volume']}\n"
formatted += f"  Avg Position: {metrics['avg_position']}\n"
return formatted

async def run_analysis(self) -> str:
"""Execute full AEO analysis and generate recommendations"""
print(f"\n[{datetime.now().isoformat()}] Starting AEO analysis for {self.domain}")

# Gather data from SearchFit MCP
visibility = await self.check_visibility()
trends = await self.analyze_performance()
gaps = await self.identify_content_gaps()

# Format data for Claude
analysis_prompt = f"""
You are an expert Answer Engine Optimization (AEO) strategist. Analyze the following 
AI search visibility data and provide actionable recommendations.

{self._format_visibility_data(visibility)}

30-Day Trends:
{json.dumps(trends, indent=2)}

Content Gaps:
{json.dumps(gaps, indent=2)}

Provide:
1. Summary of visibility performance
2. Engines where visibility is below 30% (critical alerts)
3. Top 3 content gaps to address
4. Specific recommendations for the next 7 days
5. Predicted impact of recommended changes
"""

# Use Claude to analyze and generate insights
response = self.anthropic_client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1500,
messages=[
{"role": "user", "content": analysis_prompt}
]
)

return response.content[0].text

Add Decision Logic:

Enhance your agent to make autonomous decisions based on thresholds:

async def check_and_alert(self) -> list:
"""Check visibility and generate alerts for critical drops"""
visibility = await self.check_visibility()
alerts = []

for engine, metrics in visibility.items():
citation_rate = metrics['citation_rate']
if citation_rate < self.visibility_threshold:
alerts.append({
"severity": "critical",
"engine": engine,
"message": f"Visibility in {engine} is {citation_rate:.1%} (below {self.visibility_threshold:.1%} threshold)",
"timestamp": datetime.now().isoformat()
})
elif citation_rate < self.visibility_threshold + 0.15:
alerts.append({
"severity": "warning",
"engine": engine,
"message": f"Visibility in {engine} approaching threshold: {citation_rate:.1%}",
"timestamp": datetime.now().isoformat()
})

return alerts

For deeper guidance on agent architecture and decision-making patterns, consult The Complete Guide to Building MCP Agents which covers tool discovery, resource management, and agent orchestration in detail.

Step 4: Implement Real-Time Monitoring and Scheduling

A static agent that runs once isn't useful. You need continuous monitoring that checks visibility daily, detects anomalies, and alerts your team.

Add Scheduling with APScheduler:

pip install apscheduler
from apscheduler.schedulers.asyncio import AsyncIOScheduler
from apscheduler.triggers.cron import CronTrigger
import asyncio

class MonitoredAEOAgent(AEOAgent):
def __init__(self, domain: str):
super().__init__(domain)
self.scheduler = AsyncIOScheduler()
self.alert_history = []

def add_daily_check(self, hour: int = 9, minute: int = 0):
"""Schedule daily visibility check at specified time"""
self.scheduler.add_job(
self.run_daily_check,
CronTrigger(hour=hour, minute=minute),
id='daily_aeo_check',
name='Daily AEO Visibility Check'
)
print(f"Scheduled daily check at {hour:02d}:{minute:02d} UTC")

async def run_daily_check(self):
"""Execute daily monitoring routine"""
alerts = await self.check_and_alert()

if alerts:
print(f"\n🚨 ALERTS for {self.domain}:")
for alert in alerts:
print(f"  [{alert['severity'].upper()}] {alert['message']}")
self.alert_history.append(alert)
else:
print(f"✓ {self.domain} visibility stable")

# Run full analysis weekly
if datetime.now().weekday() == 0:  # Monday
analysis = await self.run_analysis()
print(f"\nWeekly Analysis:\n{analysis}")

def start_monitoring(self):
"""Start the scheduler"""
self.scheduler.start()
print("AEO Agent monitoring started")

def stop_monitoring(self):
"""Stop the scheduler"""
self.scheduler.shutdown()
print("AEO Agent monitoring stopped")

Create a Main Execution Script:

async def main():
agent = MonitoredAEOAgent(domain="yourdomain.com")
await agent.initialize()

# Schedule daily checks at 9 AM UTC
agent.add_daily_check(hour=9, minute=0)

# Run one check immediately
await agent.run_daily_check()

# Start continuous monitoring
agent.start_monitoring()

try:
# Keep the agent running
while True:
await asyncio.sleep(1)
except KeyboardInterrupt:
agent.stop_monitoring()
print("Agent shut down gracefully")

if __name__ == "__main__":
asyncio.run(main())

Step 5: Integrate Notifications and Reporting

An agent that detects problems but doesn't alert anyone is useless. Implement notification channels to reach your team.

Add Slack Notifications:

pip install slack-sdk
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError

class SlackNotifier:
def __init__(self, bot_token: str, channel: str):
self.client = WebClient(token=bot_token)
self.channel = channel

def send_alert(self, domain: str, alerts: list) -> bool:
"""Send critical alerts to Slack"""
if not alerts:
return True

critical_alerts = [a for a in alerts if a['severity'] == 'critical']

if not critical_alerts:
return True

message = f"🚨 *AEO Alert for {domain}*\n\n"
for alert in critical_alerts:
message += f"• {alert['message']}\n"

try:
self.client.chat_postMessage(
channel=self.channel,
text=message
)
return True
except SlackApiError as e:
print(f"Slack notification failed: {e}")
return False

def send_weekly_report(self, domain: str, analysis: str) -> bool:
"""Send weekly AEO analysis to Slack"""
try:
self.client.chat_postMessage(
channel=self.channel,
blocks=[
{
"type": "header",
"text": {
"type": "plain_text",
"text": f"📊 Weekly AEO Report: {domain}"
}
},
{
"type": "section",
"text": {
"type": "mrkdwn",
"text": analysis
}
}
]
)
return True
except SlackApiError as e:
print(f"Slack report failed: {e}")
return False

Integrate Notifications into Your Agent:

class NotifiedAEOAgent(MonitoredAEOAgent):
def __init__(self, domain: str, slack_token: str = None, slack_channel: str = None):
super().__init__(domain)
self.slack_notifier = SlackNotifier(slack_token, slack_channel) if slack_token else None

async def run_daily_check(self):
"""Execute daily check with notifications"""
alerts = await self.check_and_alert()

if self.slack_notifier:
self.slack_notifier.send_alert(self.domain, alerts)

# Weekly analysis
if datetime.now().weekday() == 0:
analysis = await self.run_analysis()
if self.slack_notifier:
self.slack_notifier.send_weekly_report(self.domain, analysis)

Step 6: Connect to SearchFit's Analytics Dashboard

While your custom agent is powerful, it should also feed data back into SearchFit's analytics platform for centralized visibility tracking.

Push Agent Insights to SearchFit:

class SearchFitIntegratedAgent(NotifiedAEOAgent):
async def push_insights_to_searchfit(self, insights: dict):
"""Send agent-generated insights back to SearchFit"""
payload = {
"domain": self.domain,
"timestamp": datetime.now().isoformat(),
"agent_insights": insights,
"alerts": self.alert_history[-10:]  # Last 10 alerts
}

response = await self.mcp_client.call_tool(
tool_name="push_agent_insights",
params=payload
)

return response

async def run_daily_check(self):
"""Enhanced daily check that also pushes insights"""
alerts = await self.check_and_alert()
visibility = await self.check_visibility()

insights = {
"alert_count": len(alerts),
"critical_alerts": len([a for a in alerts if a['severity'] == 'critical']),
"visibility_snapshot": visibility,
"timestamp": datetime.now().isoformat()
}

await self.push_insights_to_searchfit(insights)

if self.slack_notifier:
self.slack_notifier.send_alert(self.domain, alerts)

This creates a feedback loop where your custom agent and SearchFit's platform continuously inform each other, giving you both real-time alerting and historical trend analysis.

Step 7: Test, Deploy, and Iterate

Write Unit Tests:

import pytest
from unittest.mock import AsyncMock, patch

@pytest.mark.asyncio
async def test_visibility_check():
agent = AEOAgent("example.com")
agent.mcp_client.call_tool = AsyncMock(return_value={
"chatgpt": {"citation_rate": 0.45, "search_volume": 1200},
"perplexity": {"citation_rate": 0.52, "search_volume": 890}
})

visibility = await agent.check_visibility()
assert visibility["chatgpt"]["citation_rate"] == 0.45

@pytest.mark.asyncio
async def test_alert_generation():
agent = AEOAgent("example.com")
agent.mcp_client.call_tool = AsyncMock(return_value={
"chatgpt": {"citation_rate": 0.15},  # Below threshold
"perplexity": {"citation_rate": 0.50}
})

alerts = await agent.check_and_alert()
assert len(alerts) >= 1
assert any(a['severity'] == 'critical' for a in alerts)

Deploy as a Container:

Create a Dockerfile for easy deployment:

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "main.py"]

Production Checklist:

  • Store all credentials in environment variables or secrets manager
  • Add comprehensive logging (use logging module, not print statements)
  • Implement error handling and retry logic for MCP calls
  • Set up monitoring for the agent itself (is it running? Are calls failing?)
  • Use a message queue (Redis, RabbitMQ) for reliability at scale
  • Test with multiple domains before scaling
  • Document your agent's behavior and MCP tool usage
  • Set up alerting if the agent itself fails

Advanced: Building Multi-Domain Agents

Once you've mastered single-domain monitoring, scaling to multiple domains requires architectural changes.

Use a Configuration File:

domains:
- name: "yourdomain.com"
slack_channel: "#aeo-alerts"
visibility_threshold: 0.30
check_frequency: "daily"
- name: "yourbrand.com"
slack_channel: "#aeo-alerts"
visibility_threshold: 0.35
check_frequency: "daily"
- name: "competitor.com"
slack_channel: "#competitive-intel"
visibility_threshold: 0.25
check_frequency: "weekly"

Create a Multi-Domain Orchestrator:

import yaml

class AEOOrchestrator:
def __init__(self, config_file: str):
with open(config_file, 'r') as f:
self.config = yaml.safe_load(f)
self.agents = {}

async def initialize_all_agents(self):
"""Create and initialize an agent for each domain"""
for domain_config in self.config['domains']:
domain = domain_config['name']
agent = NotifiedAEOAgent(
domain=domain,
slack_token=os.getenv('SLACK_BOT_TOKEN'),
slack_channel=domain_config['slack_channel']
)
agent.visibility_threshold = domain_config['visibility_threshold']
await agent.initialize()
self.agents[domain] = agent
print(f"Initialized agent for {domain}")

def start_all_monitoring(self):
"""Start monitoring for all domains"""
for domain, agent in self.agents.items():
agent.add_daily_check()
agent.start_monitoring()

Pro Tips and Common Pitfalls

⚠️ Warning: Rate Limiting SearchFit MCP has rate limits. Don't call visibility metrics more than once per hour per domain. Use caching:

from functools import lru_cache
from datetime import datetime, timedelta

class CachedAEOAgent(AEOAgent):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.cache = {}
self.cache_ttl = timedelta(hours=1)

async def check_visibility(self) -> dict:
cache_key = f"{self.domain}_visibility"
if cache_key in self.cache:
cached_data, timestamp = self.cache[cache_key]
if datetime.now() - timestamp < self.cache_ttl:
return cached_data

result = await super().check_visibility()
self.cache[cache_key] = (result, datetime.now())
return result

💡 Pro Tip: Use Claude's Tool Use for Complex Analysis Instead of writing all decision logic yourself, let Claude analyze SearchFit data. This scales better and adapts to new patterns:

async def run_intelligent_analysis(self):
"""Let Claude analyze SearchFit data and decide what to do"""
visibility = await self.check_visibility()

response = self.anthropic_client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=2000,
tools=[
{
"name": "generate_content_brief",
"description": "Create a content brief to address visibility gaps",
"input_schema": {
"type": "object",
"properties": {
"topic": {"type": "string"},
"target_engines": {"type": "array", "items": {"type": "string"}},
"urgency": {"type": "string", "enum": ["low", "medium", "high"]}
}
}
}
],
messages=[
{
"role": "user",
"content": f"Analyze this visibility data and recommend actions: {json.dumps(visibility)}"
}
]
)

return response

🔐 Pro Tip: Implement Least-Privilege Access Follow OAuth 2.1 best practices when configuring MCP permissions. Your agent should only access the specific SearchFit tools it needs. Reference MCP authorization best practices to configure scopes properly.

⚡ Pro Tip: Combine with SageMaker for Predictive Models If you want to predict visibility trends rather than just react to them, integrate AWS SageMaker. Learn how to enhance AI agents with predictive ML models to forecast which content will be cited next.

Integrating with Your Existing Stack

Your AEO agent doesn't exist in isolation. It should integrate with your existing content, analytics, and marketing tools.

Connect to Your CMS:

If you use WordPress, Contentful, or another CMS, your agent can automatically pull content metadata:

class CMSIntegratedAgent(SearchFitIntegratedAgent):
def __init__(self, domain: str, contentful_space_id: str, contentful_token: str):
super().__init__(domain)
self.contentful_space_id = contentful_space_id
self.contentful_token = contentful_token

async def get_content_inventory(self) -> list:
"""Fetch all published content from Contentful"""
headers = {"Authorization": f"Bearer {self.contentful_token}"}
url = f"https://cdn.contentful.com/spaces/{self.contentful_space_id}/entries"

async with aiohttp.ClientSession() as session:
async with session.get(url, headers=headers) as resp:
return await resp.json()

Feed Insights Back to Your Analytics Platform:

Integrate with SearchFit's AI tracking to centralize all visibility data:

async def sync_to_searchfit_dashboard(self):
"""Push agent metrics to SearchFit for unified reporting"""
metrics = {
"domain": self.domain,
"timestamp": datetime.now().isoformat(),
"visibility_by_engine": await self.check_visibility(),
"alert_count": len(self.alert_history),
"content_gaps": await self.identify_content_gaps()
}

await self.mcp_client.call_tool(
tool_name="update_dashboard_metrics",
params=metrics
)

Deploying at Scale with Multiple Teams

If you're managing AEO agents for multiple clients (as a marketing agency would), use a supervisor architecture.

Create a Supervisor Agent:

class AEOSupervisor:
def __init__(self, clients: list):
self.clients = clients  # List of {domain, team_slack_channel, thresholds}
self.agents = {}

async def initialize(self):
for client in self.clients:
agent = NotifiedAEOAgent(
domain=client['domain'],
slack_token=os.getenv('SLACK_BOT_TOKEN'),
slack_channel=client['slack_channel']
)
agent.visibility_threshold = client.get('threshold', 0.30)
await agent.initialize()
self.agents[client['domain']] = agent

async def run_all_checks(self):
"""Execute checks for all client domains in parallel"""
tasks = [agent.run_daily_check() for agent in self.agents.values()]
await asyncio.gather(*tasks)

async def generate_executive_summary(self) -> str:
"""Create a cross-client summary for leadership"""
summary = "# AEO Performance Summary\n\n"

for domain, agent in self.agents.items():
visibility = await agent.check_visibility()
avg_visibility = sum(m['citation_rate'] for m in visibility.values()) / len(visibility)
summary += f"**{domain}**: {avg_visibility:.1%} average visibility\n"

return summary

For guidance on building production-grade MCP agents at scale, review The New Standard for AI: Future-Proofing Your Agents with MCP which covers enterprise deployment patterns and reliability strategies.

Key Takeaways

Building AEO agents on top of SearchFit MCP gives you automated visibility into your brand's presence across ChatGPT, Perplexity, Gemini, Claude, Grok, and Google, and more importantly, it gives you the ability to act on that visibility in real time.

What you've learned:

  1. Connection & Authentication - Securely connect your agent to SearchFit's MCP server and discover available tools
  2. Agent Design - Define clear goals and select the right SearchFit MCP tools for your use case
  3. Core Logic - Build agents that check visibility, detect drops, and generate recommendations using Claude's reasoning
  4. Continuous Monitoring - Schedule daily checks and integrate alerting via Slack or other channels
  5. Integration - Connect your agent back to SearchFit's platform and your existing marketing stack
  6. Scaling - Deploy agents across multiple domains with configuration files and supervisor architectures

Next Steps:

  • Start with a single domain and one clear goal (e.g., "alert me if ChatGPT visibility drops below 25%")
  • Test your agent thoroughly before deploying to production
  • Use SearchFit's analytics dashboard to validate that your agent's insights match the platform's data
  • Iterate based on what you learn, your first agent won't be perfect, and that's fine
  • Once stable, expand to additional domains and more complex decision logic

Your competitors are already getting cited in AI search. With a custom AEO agent built on SearchFit MCP, you'll know exactly where you stand, why you're losing visibility, and what to do about it, before your team even asks the question.

For additional context on building AI agents with MCP, explore Build Your First AI Agent from Scratch for practical examples of integrating real-world data sources into agent workflows, or watch Building AI Agents with Model Context Protocol for a deep dive into MCP architecture and tool discovery patterns.

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