MCP Servers for Salesforce Developers

After spending a couple of decades building on the Salesforce platform, I’ve seen a lot of “revolutionary” technologies come and go. Most are just hype. But every once in a while, something actually changes how we work. The Model Context Protocol (MCP) looks to be one of those things.

If you’ve ever gotten frustrated with your AI coding assistant because it can’t see your actual Salesforce org, doesn’t know your custom metadata, or makes you copy-paste SF CLI output constantly, MCP servers are about to solve that problem.

What Are MCP Servers?

Think of MCP servers as translators between AI assistants and your actual dev tools. The Model Context Protocol is basically a way for AI to talk to your Salesforce org, your CLI tools, your APIs - whatever you use to get work done.

Before MCP, AI assistants were IMHO pretty much useless for real development work. They could look at code you pasted, but they had no idea what was actually in your org or what your deployment pipeline looked like.

MCP servers change that by letting AI:

  • Actually read your Salesforce metadata, not just guess about it
  • Run commands like SF CLI or API calls for you
  • Remember stuff about your specific org and setup
  • Stay current with what’s actually happening in your environment

So instead of that annoying back-and-forth where you paste code, get suggestions, paste more code, get more suggestions - your AI can just go look at your org and give you real answers about your actual setup.

Why MCP Exists

Here’s the problem we’ve all dealt with: AI assistants were basically blind. They could write code and explain stuff, but they had no clue about your actual environment. So you’d end up with these incredibly frustrating conversations:

# The old way - constant context switching
Developer: "Help me debug this Apex trigger"
AI: "I'd be happy to help, but I need to see the code"
Developer: *copies trigger code*
AI: "This looks like a bulkification issue. Can you show me the test class?"
Developer: *copies test class*
AI: "What's the current test coverage?"
Developer: *runs SF CLI command, copies output*

MCP servers fix this by letting AI actually see what’s going on:

# The MCP way - seamless integration
Developer: "Help me debug this Apex trigger"
AI: *reads trigger from org* "I can see the AccountTrigger has a SOQL-in-loop issue. 
    Let me check the test coverage... *queries org* Currently at 67%, below the 75% requirement.
    Here's how to fix both issues..."

The whole point is that modern development isn’t just about writing code. You’re juggling orgs, metadata, deployments, monitoring, docs and probably more. AI needs to understand all of that to actually help instead of just getting in the way.

How MCP Servers Work

The setup is actually pretty straightforward:

1. Protocol Layer

There’s a standard way for AI to talk to servers (JSON-RPC), so different AI tools can work with the same servers.

2. Server Implementation

Each server handles one thing well. For Salesforce stuff, you might have servers for:

  • Metadata API integration for reading your org structure
  • SF CLI wrapper for running deployment commands
  • Apex test execution for running and analyzing tests
  • Debug log analysis for troubleshooting issues

3. Security Model

Obviously this needs to be secure (Trust is #1!), so there are controls for:

  • Authentication (OAuth, API keys, whatever you’re using)
  • Permissions (the AI can’t do everything, just what you allow)
  • Audit logging (so you know what the AI actually did)
  • Environment separation (sandbox vs prod)

4. Capability Discovery

The AI can ask servers “what can you do?” and adapt based on what’s available. Pretty handy for dynamic setups.

Use Case 1: Intelligent Org Analysis and Documentation

Here’s where this gets really #awesome. Instead of manually digging through your org to figure out how something works, your AI can just go explore and tell you what it finds.

The Traditional Approach

Developer: "I need to understand how our Lead conversion process works"
*Manually explores custom objects*
*Manually Reviews multiple Apex classes*
*Manually Checks Process Builder flows*
*ManuallyExamines validation rules*
*Documents findings*

With MCP Servers

// MCP Server: Salesforce Metadata Analyzer
class SalesforceMetadataServer {
  async analyzeLeadConversion() {
    // Query org for Lead-related metadata
    const leadMetadata = await this.metadata.describe('Lead');
    const conversionTriggers = await this.apex.findByPattern('Lead.*Convert');
    const flows = await this.flow.findByObject('Lead');
    const validationRules = await this.metadata.getValidationRules('Lead');
    
    return {
      leadFields: leadMetadata.fields,
      conversionLogic: conversionTriggers,
      automatedProcesses: flows,
      businessRules: validationRules,
      recommendations: this.analyzePatterns()
    };
  }
}

With this setup, you could just ask: “Figure out how our Lead conversion works and document it.” The AI would:

  1. Find all the Lead stuff by querying your org’s metadata
  2. Look at the code patterns in triggers, classes, and flows
  3. Map out dependencies between different components
  4. Generate docs with diagrams and everything
  5. Suggest improvements based on best practices

Basically turns hours of detective work into a few minutes of letting the AI do the boring stuff.

Implementation Example

# MCP server endpoint for Salesforce metadata
@mcp_server.tool("analyze_object_relationships")
async def analyze_relationships(object_name: str):
    """Analyze all relationships and dependencies for a Salesforce object"""
    
    # Query metadata API for object definition
    object_metadata = await sf_client.metadata.describe(object_name)
    
    # Find all related objects through relationships
    relationships = []
    for field in object_metadata.fields:
        if field.type in ['reference', 'masterdetail']:
            relationships.append({
                'field': field.name,
                'referenced_object': field.referenceTo[0],
                'relationship_type': field.type
            })
    
    # Find Apex classes that reference this object
    apex_references = await sf_client.tooling.query(
        f"SELECT Name, Body FROM ApexClass WHERE Body LIKE '%{object_name}%'"
    )
    
    # Find flows that use this object
    flow_references = await sf_client.tooling.query(
        f"SELECT MasterLabel, Definition FROM Flow WHERE Definition LIKE '%{object_name}%'"
    )
    
    return {
        'object': object_name,
        'relationships': relationships,
        'apex_classes': [cls['Name'] for cls in apex_references['records']],
        'flows': [flow['MasterLabel'] for flow in flow_references['records']],
        'complexity_score': calculate_complexity_score(relationships, apex_references, flow_references)
    }

Use Case 2: Continuous Integration and Deployment Assistance

The other big win is having AI that actually understands your deployment pipeline. No more manually checking if your deploy worked, digging through test failures, or trying to figure out why CI is broken.

Traditional CI/CD Troubleshooting

# Developer workflow for deployment issues
git push feature/new-validation-rule
*Checks GitHub Actions status*
*Sees deployment failed*
*Logs into Salesforce org*
*Checks deployment status*
*Reviews error logs*
*Realizes test failure*
*Runs specific test class*
*Analyzes coverage reports*
*Fixes code, repeats process*

MCP-Enabled DevOps Assistant

With MCP servers hooked up to your pipeline, it’s more like:

Developer: "My latest deployment failed. What's wrong?"

AI: *queries deployment status* "Your deployment to UAT failed due to test failures in AccountTriggerTest. 
    *analyzes test results* The issue is insufficient bulkification in your new validation rule.
    *examines code coverage* This dropped your coverage from 78% to 71%.
    
    Here's the specific fix needed..."

Implementation Architecture

# MCP Server: DevOps Pipeline Integration
class SalesforceDevOpsServer:
    def __init__(self):
        self.sf_client = SFClient()
        self.github_client = GitHubClient()
        self.org_manager = OrgManager()
    
    @mcp_tool("check_deployment_status")
    async def check_deployment_status(self, deployment_id: str = None):
        """Check the status of recent deployments"""
        if not deployment_id:
            # Get most recent deployment
            deployment_id = await self.get_latest_deployment()
        
        status = await self.sf_client.force.mdapi.deploy.report(deployment_id)
        
        if status.success:
            return {"status": "success", "details": status.details}
        else:
            # Analyze failures
            failures = await self.analyze_deployment_failures(status)
            return {
                "status": "failed",
                "failures": failures,
                "suggested_fixes": await self.suggest_fixes(failures)
            }
    
    @mcp_tool("run_specific_tests")
    async def run_specific_tests(self, test_classes: list):
        """Run specific test classes and return detailed results"""
        results = await self.sf_client.force.apex.test.run(
            class_names=test_classes,
            result_format='json'
        )
        
        return {
            "test_results": results,
            "coverage_analysis": await self.analyze_coverage(results),
            "performance_insights": await self.analyze_performance(results)
        }
    
    async def analyze_deployment_failures(self, status):
        """Analyze deployment failures and categorize issues"""
        failures = []
        
        for component_failure in status.details.componentFailures:
            failure_analysis = {
                "component": component_failure.fullName,
                "type": component_failure.componentType,
                "error": component_failure.problem,
                "category": self.categorize_error(component_failure.problem),
                "line_number": component_failure.lineNumber
            }
            
            # For Apex failures, get the actual code context
            if component_failure.componentType == 'ApexClass':
                code_context = await self.get_code_context(
                    component_failure.fullName,
                    component_failure.lineNumber
                )
                failure_analysis["code_context"] = code_context
            
            failures.append(failure_analysis)
        
        return failures

Context Preservation Across Sessions

You know how annoying it is when AI forgets everything between conversations?

Session 1: "Help me optimize this SOQL query"
*Explains query optimization*

Session 2: "How's the performance now?"
AI: "I don't have context about your previous query..."

MCP servers fix that:

Session 1: "Help me optimize this SOQL query"
*AI accesses org, analyzes query, suggests optimizations*

Session 2: "How's the performance now?"
*AI checks query performance stats, compares to baseline*
"Performance improved by 34%. Execution time down from 450ms to 295ms."

Proactive Assistance

Instead of just answering questions, AI can actually keep an eye on things:

# Proactive monitoring example
@mcp_tool("monitor_org_health")
async def monitor_org_health():
    """Continuously monitor org health and suggest improvements"""
    health_metrics = {
        "api_usage": await self.check_api_limits(),
        "storage_usage": await self.check_storage_limits(),
        "test_coverage": await self.check_overall_coverage(),
        "performance_issues": await self.analyze_slow_queries(),
        "security_concerns": await self.check_security_health()
    }
    
    alerts = []
    if health_metrics["api_usage"] > 0.8:
        alerts.append("API usage at 80% - consider optimizing bulk operations")
    
    if health_metrics["test_coverage"] < 0.75:
        alerts.append("Test coverage below 75% - deployment risks increasing")
    
    return {"metrics": health_metrics, "alerts": alerts}

Getting Started: Building Your First MCP Server

Want to try this out? Here’s how to get started without going crazy:

1. Choose Your Integration Points

Pick the stuff you use every day:

  • SF CLI for deployments and org management
  • Salesforce APIs for metadata and data access
  • Git repositories for code analysis
  • CI/CD pipelines for deployment monitoring

2. Implement Basic Capabilities

# Minimal MCP server for Salesforce
from mcp import MCPServer
import asyncio

class SalesforceMCPServer(MCPServer):
    def __init__(self):
        super().__init__("salesforce-assistant")
        self.add_tool("query_metadata", self.query_metadata)
        self.add_tool("run_sf_command", self.run_sf_command)
    
    async def query_metadata(self, object_name: str):
        """Query Salesforce metadata for an object"""
        # Implementation here
        pass
    
    async def run_sf_command(self, command: str):
        """Execute SF CLI commands safely"""
        # Implementation with proper security controls
        pass

# Start the server
if __name__ == "__main__":
    server = SalesforceMCPServer()
    asyncio.run(server.serve())

3. Security Considerations

Don’t skip the security stuff:

class SecureSalesforceServer(MCPServer):
    def __init__(self):
        super().__init__()
        self.allowed_commands = [
            'sf org list',
            'sf project retrieve status',
            'sf apex test run'
        ]
        self.prohibited_patterns = [
            'data query',             # Prevent data extraction
            'org create user',        # Prevent user management
            'org delete'              # Prevent destructive actions
        ]
    
    async def validate_command(self, command: str) -> bool:
        """Validate commands before execution"""
        if not any(cmd in command for cmd in self.allowed_commands):
            return False
        
        if any(pattern in command for pattern in self.prohibited_patterns):
            return False
        
        return True

What’s Coming Next?

We’re still early with MCP servers, but you can see where this might be headed. AI that actually understands your whole development setup, not just isolated pieces of code.

For Salesforce developers, we’re talking about:

  • AI that knows your org - understands your specific setup and limits
  • Proactive monitoring - catches problems before they bite you
  • Smart deployment help - predicts issues and suggests fixes
  • Auto-updating docs - keeps documentation current as your code changes
  • Cross-platform integration - connects Salesforce work with everything else you use

Start small, though. Pick one annoying workflow and make it better. Then build from there as you figure out what actually helps your team.

Embracing the Integration Future

This is a big shift in how AI helps with development. Instead of being this external thing you have to constantly explain stuff to, AI becomes like a teammate who actually gets your environment and your problems.

For Salesforce developers, we’re finally getting AI that understands platform complexity - governor limits, sharing models, deployment pipelines, org management, all of it. The result? You get more done, write better code, and solve problems faster.

The goal isn’t to replace developers (that’s not happening). It’s about making AI smart enough to actually help with the complex stuff we deal with every day. MCP servers get us there.