🌩️ Salesforce MCP Server

This MCP (Model Context Protocol) server provides AI-powered tools for interacting with Salesforce, featuring opportunity intelligence, conversation analysis, business case generation, and standard CRUD operations. Built with pagination support and optimized response structures for AI workflows.

🎯 What This Means for You

Imagine having a sales expert that instantly knows everything about your deals and can give you winning strategies. That's what this AI does with your Salesforce data.

💬 Just Chat Naturally - No Technical Skills Needed

You say: "Show me all Microsoft deals over $100K in the proposal stage"
AI does: Searches your entire Salesforce database and returns matching opportunities

You say: "How's the engagement on the Acme deal? Are we talking to the right people?"
AI does: Analyzes all your calls, emails, and activities to give engagement insights

You say: "Find me deals similar to this opportunity that we won"
AI does: Scans your database for similar successful deals and success patterns

You say: "Create a business case document for this opportunity"
AI does: Generates a professional PDF ready for executives

🚀 Real Conversation Examples

Scenario: "I'm struggling with the Acme Corp deal. It's been stuck in negotiation for 2 months."

AI Response: "Let me analyze the Acme opportunity... I see limited engagement in the past 3 weeks. You've had 2 calls with procurement but haven't connected with the decision maker yet. Similar deals that got unstuck had executive sponsor engagement. I found 3 comparable deals where scheduling an executive briefing moved things forward. Here's a business case document you can use..."

Scenario: "My manager wants to know why our cloud migration deals are taking so long to close."

AI Response: "Analyzing your cloud migration opportunities... The average sales cycle is 40% longer than industry standard. The pattern shows deals stall in the technical evaluation stage. Successful cloud deals had solution architects engaged 2 weeks earlier on average. I recommend involving technical resources sooner..."

✨ What You Get

• 🔍 Smart Search: Find any opportunity with natural language
• 💬 Engagement Analysis: Know who you're talking to and who you're missing
• 🧠 Strategic Insights: Get recommendations based on similar successful deals
• 📊 Pipeline Intelligence: Understand your pipeline health and conversion patterns
• 📋 Professional Documents: Generate business cases and executive summaries
• 📈 Performance Analytics: See what's working and what needs attention

The Magic: You just chat naturally. The AI automatically connects to your Salesforce, analyzes millions of data points, finds patterns, and gives you actionable recommendations. It's like having a sales intelligence team, data analyst, and business consultant all rolled into one AI that knows your Salesforce inside and out.

💬 Live Chat Example

🤔 What is MCP?

The Model Context Protocol (MCP) is a standardized way for AI models to interact with external tools and resources. MCP servers provide specific capabilities that can be used by AI models through a consistent interface. This Salesforce MCP server allows AI models to interact with Salesforce data and operations in a structured way.

🚀 Installation

npm (recommended)

# Run directly with npx
npx @aaronsb/salesforce-cloud-mcp

# Or install globally
npm install -g @aaronsb/salesforce-cloud-mcp

From Source

git clone https://github.com/aaronsb/salesforce-cloud.git
cd salesforce-cloud
npm install
npm run build
node build/index.js

⚙️ Configuration

The server requires configuration in your Claude desktop app's configuration file. On Linux, this is located at ~/.config/Claude/claude_desktop_config.json. On macOS, it's at ~/Library/Application Support/Claude/claude_desktop_config.json.

Add the following configuration to the mcpServers object in your config file:

{
  "mcpServers": {
    "salesforce-cloud": {
      "command": "node",
      "args": ["/path/to/salesforce-cloud/build/index.js"],
      "env": {
        "SF_CLIENT_ID": "your_client_id",
        "SF_CLIENT_SECRET": "your_client_secret",
        "SF_USERNAME": "your_salesforce_username",
        "SF_PASSWORD": "your_salesforce_password",
        "SF_LOGIN_URL": "https://login.salesforce.com"
      }
    }
  }
}

Required Environment Variables

• SF_CLIENT_ID: Your Salesforce OAuth client ID
• SF_CLIENT_SECRET: Your Salesforce OAuth client secret
• SF_USERNAME: Your Salesforce username
• SF_PASSWORD: Your Salesforce password
• SF_LOGIN_URL: Salesforce login URL (optional, defaults to https://login.salesforce.com)

To obtain these credentials:

1. Go to Setup in your Salesforce org
2. Navigate to App Manager
3. Create a new Connected App
4. Enable OAuth settings
5. Add necessary OAuth scopes
6. Save and wait for activation
7. Copy the generated Consumer Key (Client ID) and Consumer Secret (Client Secret)

🎯 Working with Custom Fields

When constructing queries or working with Salesforce data, it's important to understand that many fields referenced may be custom fields specific to your Salesforce instance. Here's what you need to know:

Understanding Custom Fields

• Custom fields in Salesforce end with __c in their API names
• What appears as "Implementation Status" in the UI might be stored as "Implementation_Status__c"
• Custom fields can represent organization-specific business concepts

Best Practices

1. Object Metadata Analysis:

   • Use the describe_object tool with includeFields: true to examine both standard and custom fields
   • Look for fields that match your intent in both standard and custom field lists
   • Map user-friendly field names to their actual API names

2. Field Type Consideration:

   • Custom fields can be various types (text, picklist, lookup, etc.)
   • Understanding field types helps construct appropriate queries
   • Custom fields might reference other custom objects through lookup relationships

3. Query Construction:

   • Build queries that can handle both standard and custom fields
   • Use field metadata to validate field existence before querying
   • Consider relationships between objects, especially with custom lookup fields

Example

If searching for "Project Status":

1. First, examine the object's fields to find the actual field name:

{
  "objectName": "Opportunity",
  "includeFields": true
}

2. Look for fields like "Project_Status__c" or similar custom fields that match your intent
3. Use the discovered field name in your queries:

{
  "query": "SELECT Id, Name, Project_Status__c FROM Opportunity"
}

🛠️ Tools

Core Salesforce Operations

search_fields

Find the field(s) that carry a concept when you know what you want to query but not the API name. Searches the discovered field catalog (ADR-302) by keyword across field API names, labels, and help text, ranked by match strength.

{
  term: string;             // Required: matched against field name, label, help text
  objectName?: string;      // Optional: scope to one object (default: all core objects)
  includeValues?: boolean;  // Optional: return active picklist values for matches (default: false)
  minPopulationPct?: number;// Optional: drop fields below this population density (0-100)
  limit?: number;           // Optional: max matches (default: 25, max: 100)
}

Example:

{
  "term": "ai",
  "objectName": "Opportunity",
  "includeValues": true
}

The match is lexical, not semantic: it finds fields whose metadata contains the term, so a concept the schema names differently won't surface — read salesforce://field-catalog/{objectName}/all to browse everything.