Ghostshell
The Subconscious AI MCP Server is a tool based on the Model Context Protocol that allows users to run AI - driven conjoint experiments through AI assistants such as Claude and Cursor. It uses causal inference and synthetic population data for decision - making analysis and provides REST API and real - time update functions.
rating : 2.5 points
downloads : 6.2K
What is the Subconscious AI MCP Server?
This is a server specifically designed for AI assistants, allowing you to design and run professional market research experiments through simple conversations. It uses advanced causal inference methods and synthetic populations based on US census data to help you understand why people make specific choices, such as why they choose a particular product, service, or brand.How to use the Subconscious AI MCP Server?
You only need to configure the server address in a supported AI assistant (such as Claude Desktop or Cursor), and then you can use natural language conversations to: 1) Verify whether the research question is suitable for causal analysis; 2) Design experimental attributes; 3) Run the experiment and collect data; 4) Obtain analysis results and insights. The entire process is completed entirely through conversations without writing code.Use cases
Suitable for professionals such as product managers, market researchers, entrepreneurs, and UX designers who need to understand user preferences and decision - making factors. Particularly suitable for scenarios that require quantifying user preferences, such as new product feature testing, pricing strategy research, brand positioning analysis, advertising effectiveness evaluation, and policy impact prediction.Main features
๐ง Causal research verification
Before starting an experiment, the AI will verify whether your research question is suitable for causal analysis to ensure the scientific nature of the research design.
๐ฅ Synthetic population simulation
Create a representative population sample based on US census microdata (IPUMS) to ensure the statistical validity of the experimental results.
๐ Conjoint analysis experiment
Use the AMCE (Average Marginal Component Effect) method to accurately measure the relative importance of different attributes to user decisions.
๐ค MCP protocol integration
Seamlessly integrate into AI assistants that support the MCP protocol, such as Claude Desktop and Cursor, and operate through conversations.
๐ REST API access
Provide a direct HTTP API interface that can be integrated with automation tools such as n8n and Zapier or custom applications.
โก Real - time progress updates
Provide real - time updates on the experiment progress through Server - Sent Events (SSE) to keep track of the experiment status at any time.
Advantages
No programming skills required: Operate entirely through natural language conversations, reducing the usage threshold.
Rapid experiment deployment: Design and start an experiment within minutes, while traditional methods take days.
Cost - effective: Significantly reduce time and money costs compared to traditional market research.
Statistical validity: Based on real population data, ensuring that the results are statistically representative.
Flexible integration: Support multiple AI assistants and automation tools to adapt to different workflows.
Limitations
Subscription required: You must have a valid subscription to Subconscious AI to use it.
Network dependency: A stable network connection is required to access the server.
Data limitation: Synthetic populations are mainly based on US population data, and data for other countries is limited.
AI dependency: Experiment design and analysis rely on the understanding ability of the AI model.
Learning curve: You need to understand basic market research concepts to use it effectively.
How to use
Get an access token
Visit the Subconscious AI platform (app.subconscious.ai) and generate your personal access token in the settings.
Configure the AI assistant
Depending on the AI assistant you use, add MCP server settings to the configuration file. For Claude Desktop, edit the configuration file; for Cursor, edit the mcp.json file.
Add server configuration
Add the URL of the Subconscious AI server and your access token to the configuration file.
Restart the AI assistant
After saving the configuration file, restart your AI assistant (Claude Desktop or Cursor) for the configuration to take effect.
Start a conversation experiment
Start a conversation in the AI assistant and use the available tools for causal verification, experiment design, and result analysis.
Usage examples
Research on electric vehicle purchase decisions
An automobile manufacturer wants to understand the key factors influencing consumers' decisions to buy electric vehicles in order to optimize product design and marketing strategies.
Analysis of user preferences for food delivery platforms
A food delivery platform wants to understand which factors users value most when choosing a platform in order to improve services and increase user stickiness.
Pricing strategy for subscription services
A SaaS company wants to test the impact of different pricing plans on users' willingness to subscribe in order to determine the optimal price point.
Frequently Asked Questions
Do I need a background in statistics or market research to use this tool?
How long does it take to complete an experiment?
What is the difference between synthetic populations and real populations?
Can I export the experimental data for analysis?
What if my research question is not suitable for causal analysis?
Which countries and regions are the samples supported for?
Related resources
Subconscious AI platform
The official web platform provides more experiment configuration options and visualization analysis tools.
Complete API documentation
Detailed API reference documentation, including a complete description of all endpoints and parameters.
MCP protocol specification
Official specification and description of the Model Context Protocol.
Introduction to the conjoint analysis method
Detailed introduction and principle explanation of the conjoint analysis method on Wikipedia.
GitHub repository
Open - source code repository, including server source code and deployment guide.

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