Lead Qualifier
A lightweight MCP tool based on ChatGPT that screens potential customers through the BANT mechanism (Budget, Authority, Need, Timeline) and guides users to input information step by step.
rating : 2.5 points
downloads : 23
What is the Lead Qualifier MCP Server?
The Lead Qualifier MCP Server is a lightweight tool that uses artificial intelligence technology to help sales teams quickly evaluate potential customer leads. It guides users step - by - step to input key information such as budget, decision - making power, needs, and timeline according to the BANT framework, thereby generating detailed scores and suggestions.How to use the Lead Qualifier MCP Server?
Simply follow the prompts to input relevant information step by step, and the system will automatically analyze and provide preliminary evaluation results. You can operate directly on the web interface without complex configuration.Applicable Scenarios
Suitable for enterprises that need to efficiently manage customer leads, especially teams that hope to improve sales efficiency through standardized processes.Main Features
BANT Information ExtractionCollect the customer's budget, authority, needs, and timeline item by item through chat - style conversations.
Smooth Interaction ExperienceOnly one question needs to be answered at each step to ensure a simple and efficient user experience.
Real - time Conversation TrackingSupports local memory storage and can be extended to a Redis database to enhance persistence capabilities.
Compatible with Multiple PlatformsSupports MCP protocols such as Dify and Cursor and can be seamlessly integrated through the SSE interface.
Advantages and Limitations
Advantages
Easy to use: Guide users step by step to complete lead evaluation.
Intelligent scoring: Generate objective scores based on the BANT framework.
High flexibility: Support multiple integration methods and expansion options.
Limitations
Depends on API key: Requires an OpenAI API Key to be configured to run.
High real - time requirement: Has a certain dependence on the network environment.
How to Use
Installation and Startup
Download and install project dependencies, then start the server.
Configure API Key
Add your OpenAI API Key to the.env file.
Deploy to Production Environment
Use ngrok or other tools to expose the server address.
Usage Examples
Case 1: Enter BudgetWhen the user enters the budget, the system will further ask permission questions.
Case 2: Complete ProcessAfter the user enters the budget, authority, needs, and timeline in sequence, the system will generate a comprehensive score.
Frequently Asked Questions
How to configure the OpenAI API Key?
Does it support custom fields?
How to improve the scoring accuracy?
Related Resources
Official Documentation
Comprehensively understand the operation guide of the Lead Qualifier MCP Server.
GitHub Code Repository
Get the source code and participate in contributions.
Video Tutorial
Quickly master the tool's usage method.
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