
Salesforce Cloud
io.github.aaronsb由 GitHub 帳號 aaronsb 發布v0.8.2更新於 Oct 24, 2026
AI-powered Salesforce CRM: opportunity intelligence, conversation analysis, SOQL, analytics
概覽
在本機執行的 MCP 伺服器,將 AI 助理連接到 Salesforce CRM,用於商機搜尋、SOQL 查詢、互動分析、銷售管道洞察與記錄新增修改刪除。
- 可以做什麼
- 搜尋與檢視 Salesforce 資料:依關鍵字尋找欄位、描述物件、列出物件、執行分頁 SOQL 查詢並讀取目前使用者資訊。處理商機:依名稱、客戶、階段或金額搜尋,並取得含關聯記錄的完整詳情。分析交易:對話與互動分析、結合競爭情報與最佳實務的商機充實、相似交易比對以及管道與轉換率洞察。透過 TeXFlow 產生 PDF、DOCX 或 Markdown 格式的商業案例文件。對任何 Salesforce 物件建立、更新與刪除記錄。
- 適用情境
- 適合希望以自然語言查詢與分析 Salesforce 銷售管道、將目前交易與過去成功交易比較,或不必離開助理就能草擬商業案例的業務與營收團隊。也適合需要透過對話臨時執行 SOQL 查詢與編輯記錄的使用者。
- 執行需求
- 以 npm 套件形式在本機執行(需要 Node.js),並在桌面應用程式的設定檔中設定。需要一個啟用 OAuth 的 Salesforce Connected App,提供 SF_CLIENT_ID、SF_CLIENT_SECRET 與 SF_LOGIN_URL;密碼流程另需 SF_USERNAME 與 SF_PASSWORD,否則採用用戶端憑證流程。需要能連線至 Salesforce 組織的網路存取。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Salesforce Cloud,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
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.
來源:README.md,提交 7f99f6e
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1- v0.8.2最新Sep 16, 2026
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