Building AI Agent Workflows with MCP: From Simple to Complex

MCP unlocks a new paradigm: AI agents that can execute complex, multi-step workflows autonomously. This guide explores how to build powerful agent workflows using MCP.

What Are AI Agent Workflows?

Traditional AI Interaction:

You: "What's the status of PR #123?"
AI: "I don't have access to GitHub."
You: *Opens GitHub, copies info*
You: "Here's the PR info: ..."
AI: "Based on that, the PR looks good."

AI Agent Workflow with MCP:

You: "What's the status of PR #123 and is it ready to merge?"
AI Agent:
  1. Uses GitHub MCP to fetch PR details
  2. Checks CI status
  3. Reviews required approvals
  4. Analyzes code changes
  5. Provides comprehensive answer
  → "PR #123 is ready to merge! ✅"

Simple Workflows

Workflow 1: Code Review Assistant

User Goal: Get AI to review a PR and suggest improvements.

Workflow Steps:

1. User: "Review PR #456 in api-server repo"
2. AI uses GitHub MCP:
   - Fetch PR metadata
   - Get list of changed files
   - Read file diffs
3. AI analyzes code
4. AI uses GitHub MCP:
   - Post review comments
   - Request changes or approve
5. User receives notification

How to Enable:

Just deploy GitHub MCP and ask:

"Review PR #456 and leave detailed comments on any issues you find"

AI automatically:

• Fetches PR
• Analyzes code
• Finds issues (security, performance, style)
• Posts structured review

Workflow 2: Database Health Check

User Goal: Daily database health report.

Workflow Steps:

1. User: "Generate daily database health report"
2. AI uses PostgreSQL MCP:
   - Check table sizes
   - Find slow queries
   - Check index usage
   - Identify missing indexes
   - Check connection pool status
3. AI analyzes metrics
4. AI uses Filesystem MCP:
   - Save report to reports/db-health-2025-07-20.md
5. AI uses Slack MCP (future):
   - Post summary to #engineering channel

Schedule with Cron:

# Every day at 9 AM
0 9 * * * curl -X POST https://your-automation-endpoint \
  -d '{"prompt": "Generate and post daily database health report"}'

Workflow 3: Documentation Updates

User Goal: Keep docs synchronized with code changes.

Workflow:

1. User commits code to main branch
2. GitHub webhook triggers AI agent
3. AI uses Filesystem MCP:
   - Read changed files
   - Identify new functions/APIs
4. AI generates documentation
5. AI uses GitHub MCP:
   - Create docs update branch
   - Commit changes
   - Open PR for review

Implementation:

// Webhook handler
app.post('/webhook/code-change', async (req, res) => {
  const { files } = req.body;

  const prompt = `
    These files changed: ${files.join(', ')}

    Read them, identify new public APIs,
    update the API documentation accordingly,
    and create a PR with the changes.
  `;

  await ai.ask(prompt);

  res.json({ status: 'processing' });
});

Intermediate Workflows

Workflow 4: Automated Bug Triage

Scenario: New GitHub issue created.

Complex Workflow:

1. GitHub webhook: New issue #789 created
2. AI uses GitHub MCP:
   - Read issue title and description
   - Check for stack trace
3. AI uses Filesystem MCP:
   - Search codebase for related files
   - Find similar past issues
4. AI analyzes:
   - Severity assessment
   - Affected components
   - Likely root cause
5. AI uses GitHub MCP:
   - Add labels (bug, backend, high-priority)
   - Assign to relevant team
   - Add comment with initial analysis
6. AI uses Slack MCP:
   - Notify team in #bugs channel

Benefits:

• Immediate issue categorization
• Faster response time
• Better issue routing
• Context for developers

Workflow 5: Release Preparation

User Goal: Prepare a new release.

Multi-Step Workflow:

User: "Prepare release v2.5.0"

AI Agent executes:

1. Validation Phase
   - GitHub MCP: Check all PRs for milestone merged
   - GitHub MCP: Verify CI passing on main branch
   - PostgreSQL MCP: Check for pending migrations

2. Changelog Generation
   - GitHub MCP: Fetch all PRs since last release
   - AI: Generate categorized changelog
   - Filesystem MCP: Update CHANGELOG.md

3. Version Bumping
   - Filesystem MCP: Update version in package.json
   - Filesystem MCP: Update version constants

4. Testing Verification
   - GitHub MCP: Check latest test run status
   - If failed: Report issues and stop
   - If passed: Continue

5. Branch Creation
   - GitHub MCP: Create release branch release/v2.5.0
   - GitHub MCP: Commit changes
   - GitHub MCP: Open release PR

6. Documentation
   - Filesystem MCP: Generate migration guide
   - GitHub MCP: Update release PR description

7. Notification
   - Slack MCP: Post to #releases channel
   - Email MCP: Notify stakeholders

Result: Complete release preparation in 2 minutes

Workflow 6: Customer Support Automation

Scenario: Customer reports login issue.

Diagnostic Workflow:

Support Agent: "Customer john@example.com can't login"

AI Agent investigates:

1. User Lookup
   - PostgreSQL MCP: Find user by email
   - Extract: user_id, account_status, created_at

2. Recent Activity
   - PostgreSQL MCP: Check login attempts (last 24h)
   - PostgreSQL MCP: Check session records

3. Account Status
   - Stripe MCP: Check subscription status
   - Find: Payment failed 3 days ago

4. Email Verification
   - PostgreSQL MCP: Check email_verified flag
   - Status: Verified ✓

5. Root Cause Analysis
   - AI determines: Subscription expired due to failed payment
   - Account auto-suspended per policy

6. Resolution Suggestions
   - Option A: Update payment method
   - Option B: Extend trial 7 days

7. Action Taken
   - PostgreSQL MCP: Update trial_extended_until
   - Email MCP: Send payment update reminder

8. Report to Support Agent
   - Summary of findings
   - Actions taken
   - Next steps

Total time: 30 seconds

Advanced Workflows

Workflow 7: Intelligent CI/CD Pipeline

Scenario: Code pushed to staging branch.

Autonomous Pipeline:

1. GitHub webhook: Push to staging

2. Pre-deployment Checks
   - GitHub MCP: Verify all required approvals
   - GitHub MCP: Check CI status
   - PostgreSQL MCP: Verify staging DB healthy

3. Deployment Planning
   - Filesystem MCP: Check for DB migrations
   - If migrations exist:
     - PostgreSQL MCP: Backup staging DB
     - PostgreSQL MCP: Run migrations (dry-run)
     - Verify success

4. Deployment Execution
   - Kubernetes MCP: Update deployment
   - Monitor rollout progress
   - Check pod health

5. Post-Deployment Validation
   - HTTP MCP: Health check endpoints
   - PostgreSQL MCP: Run smoke tests queries
   - Monitoring MCP: Check error rates

6. Regression Testing
   - Playwright MCP: Run E2E tests
   - If failures:
     - GitHub MCP: Create rollback PR
     - Slack MCP: Alert team
     - Stop process

7. Success Actions
   - GitHub MCP: Comment on related PRs
   - Slack MCP: Notify #deployments
   - Monitoring MCP: Set deployment marker

8. Documentation
   - Filesystem MCP: Update deployment log
   - Record metrics (time, changes, tests)

Completely autonomous deployment with verification!

Workflow 8: Security Incident Response

Scenario: Security scan detects vulnerability.

Rapid Response Workflow:

1. Alert Received
   - Snyk/Dependabot: CVE-2025-12345 in package X

2. Impact Assessment
   - Filesystem MCP: Find all usages of package X
   - GitHub MCP: Check which services affected
   - AI analyzes: Critical services exposed

3. Immediate Mitigation
   - Kubernetes MCP: Scale down affected services
   - Network MCP: Update firewall rules
   - Slack MCP: Alert security team (priority: P0)

4. Patch Preparation
   - NPM MCP: Find patched version
   - Filesystem MCP: Update package.json
   - GitHub MCP: Create fix branch

5. Testing
   - Run tests in CI
   - Deploy to isolated environment
   - Verify vulnerability fixed

6. Emergency Deployment
   - If tests pass:
     - Deploy to production
     - Monitor closely
   - If tests fail:
     - Escalate to human engineers

7. Post-Incident
   - GitHub MCP: Create incident report issue
   - Documentation MCP: Update security playbook
   - Calendar MCP: Schedule post-mortem

Time from detection to fix: <15 minutes

Workflow 9: Data Pipeline Monitoring

Scenario: Automated data quality monitoring.

Continuous Monitoring Workflow:

Every hour:

1. Data Freshness Check
   - PostgreSQL MCP: Check latest record timestamp
   - If >2 hours old: Alert

2. Data Quality Validation
   - PostgreSQL MCP: Run quality checks
     - Check for NULLs in required fields
     - Validate foreign key integrity
     - Check for duplicates
     - Verify data types

3. Anomaly Detection
   - PostgreSQL MCP: Compare to historical patterns
   - AI: Identify unusual trends
   - Examples:
     - Sudden spike in failed transactions
     - Drop in daily signups
     - Unusual geographic distribution

4. Root Cause Analysis (if anomaly found)
   - PostgreSQL MCP: Query related tables
   - Logs MCP: Search application logs
   - Monitoring MCP: Check system metrics
   - AI: Correlate findings

5. Automated Remediation
   - If known issue:
     - Execute fix script
     - Verify resolution
   - If unknown:
     - Create detailed incident report
     - Alert data engineering team

6. Reporting
   - Filesystem MCP: Update dashboard data
   - Slack MCP: Post hourly summary
   - If issues: Highlight in red

Workflow 10: Multi-Cloud Resource Optimization

Goal: Optimize cloud spending across AWS, Azure, GCP.

Weekly Optimization Workflow:

Every Monday:

1. Inventory Collection
   - AWS MCP: List all EC2, RDS, S3 resources
   - Azure MCP: List all VMs, DBs, storage
   - GCP MCP: List all Compute, Cloud SQL, Storage

2. Usage Analysis
   - CloudWatch MCP: Get EC2 CPU utilization (7 days)
   - Azure Monitor MCP: Get VM metrics
   - GCP Monitoring MCP: Get instance metrics

3. Cost Analysis
   - AWS Cost Explorer MCP: Get spending by service
   - Azure Cost Management MCP: Get spending
   - GCP Billing MCP: Get spending

4. Optimization Opportunities
   - AI identifies:
     - Underutilized instances (CPU <10%)
     - Over-provisioned databases
     - Unused storage volumes
     - Unattached elastic IPs
     - Old snapshots (>90 days)

5. Recommendations
   - Downsize: i3.xlarge → i3.large (save $200/mo)
   - Terminate: 15 unused volumes (save $150/mo)
   - Reserved Instances: Buy RIs for stable workloads (save 30%)
   - Lifecycle policies: Auto-delete old snapshots

6. Auto-Implement (if approved)
   - Low-risk changes: Execute automatically
   - High-risk changes: Create approval tickets

7. Reporting
   - Slack MCP: Post summary to #finops
   - Email MCP: Send exec report
   - Dashboard MCP: Update cost optimization dashboard

Monthly savings: $5,000+

Building Your Own Workflows

Design Pattern 1: Sequential Pipeline

async function sequentialWorkflow(userPrompt: string) {
  const ai = new AIAgent();

  // Step 1: Gather information
  const info = await ai.ask("Fetch PR details for PR #123");

  // Step 2: Analyze
  const analysis = await ai.ask(`Analyze this PR: ${info}`);

  // Step 3: Take action
  const action = await ai.ask(`Based on analysis, post review: ${analysis}`);

  return action;
}

Design Pattern 2: Parallel Execution

async function parallelWorkflow() {
  const ai = new AIAgent();

  // Execute multiple tasks in parallel
  const [
    ciStatus,
    codeReview,
    securityScan
  ] = await Promise.all([
    ai.ask("Check CI status for PR #123"),
    ai.ask("Review code changes in PR #123"),
    ai.ask("Run security scan on PR #123")
  ]);

  // Combine results
  const summary = await ai.ask(`
    Summarize these results:
    CI: ${ciStatus}
    Review: ${codeReview}
    Security: ${securityScan}
  `);

  return summary;
}

Design Pattern 3: Conditional Branching

async function conditionalWorkflow() {
  const ai = new AIAgent();

  const status = await ai.ask("Check if PR #123 has all approvals");

  if (status.approved) {
    // Approval path
    await ai.ask("Merge PR #123 and delete branch");
    await ai.ask("Deploy to staging");
    await ai.ask("Notify team of deployment");
  } else {
    // Rejection path
    await ai.ask("Request additional reviews");
    await ai.ask("Comment on PR with missing requirements");
  }
}

Design Pattern 4: Error Handling and Retry

async function robustWorkflow() {
  const ai = new AIAgent();

  try {
    const result = await ai.ask("Deploy to production");

    // Verify deployment
    const health = await ai.ask("Check production health");

    if (!health.healthy) {
      throw new Error("Deployment unhealthy");
    }

    return result;
  } catch (error) {
    // Rollback on failure
    await ai.ask("Rollback deployment");
    await ai.ask("Alert oncall engineer");

    // Retry with fix
    await ai.ask("Investigate failure and suggest fix");
  }
}

Design Pattern 5: Human-in-the-Loop

async function humanApprovalWorkflow() {
  const ai = new AIAgent();

  // AI prepares action
  const plan = await ai.ask("Prepare production deployment plan");

  // Ask human for approval
  const approved = await askHuman(`Approve this plan?\n${plan}`);

  if (approved) {
    await ai.ask("Execute deployment plan");
  } else {
    await ai.ask("Cancel deployment and notify team");
  }
}

Best Practices

1. Start Simple

Begin with single-tool workflows:

❌ Don't start with: "Automate entire release process"
✅ Start with: "Generate changelog from GitHub PRs"

2. Add Validation Steps

// Always verify critical actions
const result = await ai.ask("Delete old database backups");

// Verify what was deleted
const verification = await ai.ask("List remaining backups");

if (verification.count < 3) {
  throw new Error("Too many backups deleted!");
}

3. Implement Idempotency

// Workflow should be safe to run multiple times
async function idempotentWorkflow() {
  // Check if already done
  const exists = await ai.ask("Check if release branch exists");

  if (!exists) {
    await ai.ask("Create release branch");
  }

  // Continue with other steps...
}

4. Log Everything

const workflowLog = [];

async function loggedWorkflow() {
  const step1 = await ai.ask("Step 1");
  workflowLog.push({ step: 1, result: step1, timestamp: Date.now() });

  const step2 = await ai.ask("Step 2");
  workflowLog.push({ step: 2, result: step2, timestamp: Date.now() });

  // Save log for debugging
  await saveLog(workflowLog);
}

5. Set Timeouts

async function timedWorkflow() {
  const timeout = 30000; // 30 seconds

  const result = await Promise.race([
    ai.ask("Long-running task"),
    new Promise((_, reject) =>
      setTimeout(() => reject(new Error('Timeout')), timeout)
    )
  ]);

  return result;
}

Monitoring Workflows

Track Workflow Metrics

interface WorkflowMetrics {
  workflow_name: string;
  duration_ms: number;
  steps_completed: number;
  steps_failed: number;
  mcp_calls: number;
  success: boolean;
}

async function monitoredWorkflow() {
  const start = Date.now();
  const metrics: WorkflowMetrics = {
    workflow_name: 'release_preparation',
    duration_ms: 0,
    steps_completed: 0,
    steps_failed: 0,
    mcp_calls: 0,
    success: false
  };

  try {
    await executeWorkflow();
    metrics.success = true;
  } catch (error) {
    metrics.steps_failed++;
  } finally {
    metrics.duration_ms = Date.now() - start;
    await sendMetrics(metrics);
  }
}

Real-World Impact

E-commerce Company:

• Automated 80% of customer support tickets
• Response time: 4 hours → 2 minutes
• Customer satisfaction: +25%

SaaS Startup:

• Automated release process
• Release time: 4 hours → 15 minutes
• Deployment errors: -90%

Enterprise:

• Automated security response
• Incident response: 2 hours → 10 minutes
• Security posture: Significantly improved

Conclusion

MCP transforms AI from a chatbot into an autonomous agent that can execute complex workflows. Start simple and gradually build more sophisticated automations.

Key Takeaways:

• ✅ Start with simple single-tool workflows
• ✅ Add validation and error handling
• ✅ Log everything for debugging
• ✅ Monitor workflow performance
• ✅ Gradually increase complexity

Next Steps:

1. Identify a repetitive task
2. Map out the steps
3. Deploy necessary MCPs
4. Build your first workflow!

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Need help designing workflows? Email workflows@toolboost.dev