
Salesforce Cloud
io.github.aaronsbPublished by GitHub account aaronsbv0.8.2Updated Oct 24, 2026
AI-powered Salesforce CRM: opportunity intelligence, conversation analysis, SOQL, analytics
Overview
A local MCP server that connects an AI assistant to Salesforce CRM for opportunity search, SOQL queries, engagement analysis, pipeline insights and record CRUD.
- What you can do
- Search and inspect Salesforce data: find fields by keyword, describe objects, list objects, run paginated SOQL queries and read the current user's info. Work with opportunities: search by name, account, stage or amount, and fetch full details with related records. Analyze deals: engagement and conversation analysis, enrichment with competitive and best-practice context, similar-deal matching and pipeline or conversion insights. Generate business case documents as PDF, DOCX or Markdown via TeXFlow. Create, update and delete records on any Salesforce object.
- When to use it
- Useful for sales and revenue teams that want to query and analyze their Salesforce pipeline in natural language, compare deals against past wins, or draft business cases without leaving the assistant. Also suits anyone who needs ad-hoc SOQL access and record edits through chat.
- Requirements
- Runs locally as an npm package (Node.js) configured in the desktop app's config file. Needs a Salesforce Connected App with OAuth enabled, supplying SF_CLIENT_ID, SF_CLIENT_SECRET and SF_LOGIN_URL; SF_USERNAME and SF_PASSWORD are needed for the password flow, otherwise the client credentials flow is used. Network access to the Salesforce org is required.
Installation
In SourceWeft
- Open Salesforce Cloud in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
🌩️ 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)
From Source
⚙️ 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:
Required Environment Variables
SF_CLIENT_ID: Your Salesforce OAuth client IDSF_CLIENT_SECRET: Your Salesforce OAuth client secretSF_USERNAME: Your Salesforce usernameSF_PASSWORD: Your Salesforce passwordSF_LOGIN_URL: Salesforce login URL (optional, defaults to https://login.salesforce.com)
To obtain these credentials:
- Go to Setup in your Salesforce org
- Navigate to App Manager
- Create a new Connected App
- Enable OAuth settings
- Add necessary OAuth scopes
- Save and wait for activation
- 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
__cin 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
-
Object Metadata Analysis:
- Use the
describe_objecttool withincludeFields: trueto 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
- Use the
-
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
-
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":
- First, examine the object's fields to find the actual field name:
- Look for fields like "Project_Status__c" or similar custom fields that match your intent
- Use the discovered field name in your queries:
🛠️ 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.
Example:
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.
execute_soql
Execute a SOQL query with pagination support.
Example:
describe_object
Get metadata about a Salesforce object with optional field information.
Example:
create_record
Create a new record in Salesforce.
Example:
update_record
Update an existing record in Salesforce.
Example:
delete_record
Delete a record from Salesforce.
Example:
get_user_info
Get information about the current user. No parameters required.
list_objects
List all available Salesforce objects with pagination support.
Opportunity Management
search_opportunities
Search for Salesforce opportunities using flexible criteria and pattern matching.
get_opportunity_details
Get detailed information about a specific opportunity including all available fields and related records.
🧠 Opportunity Intelligence
analyze_conversation
Analyze conversation activity and engagement patterns for an opportunity. Extracts insights from calls, emails, and other activities to provide engagement recommendations.
Example:
enrich_opportunity
Enrich an opportunity with market intelligence, industry insights, and strategic recommendations based on similar deal patterns and best practices.
Example:
find_similar_opportunities
Find opportunities similar to a reference opportunity or based on specific criteria. Includes pattern analysis and similarity scoring.
Examples:
- Find similar to a reference opportunity:
- Find by criteria:
opportunity_insights
Generate detailed insights and analytics from opportunity data including pipeline health, performance metrics, and trends.
Example:
generate_business_case
Generate a professional business case document for an opportunity. Returns step-by-step instructions for creating a formatted business case using TeXFlow.
Example:
📦 Response Formats
Paginated Response
Operations that return multiple records use this format:
Error Handling
All tools return errors in a consistent format:
💻 Development
To run the server locally for development:
- Set up your environment variables in a
.envfile - Build the project:
- Start the server:
✨ Key Features
- 🧠 AI-Powered Intelligence: Advanced opportunity analysis, conversation insights, and market intelligence
- 📊 Pattern Recognition: Identify similar opportunities and success patterns in your pipeline
- 📋 Business Case Generation: Automated creation of professional business case documents using TeXFlow
- 🔍 Smart Search: Flexible opportunity search with pattern matching and filtering
- 📈 Pipeline Analytics: Detailed insights on pipeline health, conversion rates, and performance metrics
- 🔧 Standard Operations: Full CRUD operations with custom field support
- 📄 Document Integration: Seamless integration with TeXFlow MCP server for document generation
- ⚡ Optimized for AI: Response structures designed for AI model consumption
🌟 Future Possibilities
The Salesforce API offers numerous expansion opportunities:
- Support for more Salesforce objects (Leads, Cases, Contacts, etc.)
- Integration with Salesforce Flow and Process Builder
- Enhanced competitive intelligence features
- Bulk API operations for large datasets
- Chatter API integration and social selling insights
- Custom report and dashboard access
- Advanced forecasting and predictive analytics
We'd love to hear your ideas about what would be most valuable to add next!
🤝 Contributing
We welcome contributions from the community! Whether it's adding new features, improving documentation, or reporting bugs, your input helps make this project better for everyone. Feel free to submit a Pull Request or open an Issue to start a discussion.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
Source: README.md at commit 7f99f6e
Tools
0Version history
1- v0.8.2LatestSep 16, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts