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Thinkingcap
ThinkingCap is a multi-agent research server based on the model context protocol that can run multiple LLM providers in parallel and synthesize their responses to achieve comprehensive multi-angle analysis.
rating : 2.5 points
downloads : 4.2K
What is ThinkingCap?
ThinkingCap is an intelligent research server built on the Model Context Protocol (MCP). Its core concept is 'pooling wisdom' - when you pose a question, it will simultaneously query multiple different AI models (such as OpenAI's GPT, Anthropic's Claude, Google's Gemini, etc.), enabling them to conduct research from different perspectives. Finally, it synthesizes all the results into the most comprehensive and reliable answer.How to use ThinkingCap?
Using ThinkingCap is very simple. You don't need to run it directly. You only need to add a few lines of configuration to the configuration file of your commonly used AI assistant tool (such as Claude Desktop, Cursor IDE) and specify the combination of AI models you want to use. Then, when you ask your AI assistant a question, it will invoke ThinkingCap in the background, allowing multiple AI models to conduct parallel research for you.Applicable scenarios
ThinkingCap is particularly suitable for scenarios that require in-depth research, multi-angle analysis, or fact-checking. For example: writing research reports, analyzing complex problems, conducting market research, learning new knowledge, comparing different viewpoints, and obtaining the latest information (because it integrates web search functionality).Main features
Multi-agent parallel research
Simultaneously deploy multiple AI 'researchers', each of which can come from different providers (such as OpenAI, Anthropic, Google), allowing them to work in parallel and significantly reducing research time.
Support for multiple model providers
Supports almost all mainstream AI models, including OpenAI (GPT series), Anthropic (Claude series), Google (Gemini series), xAI (Grok), Groq, Cerebras, and numerous models accessible through OpenRouter.
Lightning-fast parallel execution
All specified AI models run simultaneously rather than one after another. This means you can obtain insights from multiple top-tier AIs within seconds instead of waiting for minutes.
Intelligent answer synthesis
It doesn't simply list the responses of each model. ThinkingCap analyzes, compares, removes duplicates, and integrates all the information into a coherent, comprehensive, and structured final answer.
Built-in web search
Each AI researcher will automatically use DuckDuckGo for web search when answering, ensuring that the provided answers are based on the latest and most up-to-date information without the need for additional API configuration.
Native MCP integration
Built on the Model Context Protocol, it can be seamlessly integrated into any client that supports MCP, such as Claude Desktop, Cursor, Windsurf, etc., providing a smooth user experience.
Advantages
More comprehensive and reliable answers: By synthesizing the wisdom of multiple top-tier AIs, it reduces the bias or errors of a single model.
Extremely fast research speed: Parallel queries reduce the time of traditional serial research from minutes to seconds.
Extremely convenient to use: No software installation is required. You can use it by simply adding a few lines of code to the configuration file.
Access to real-time information: The built-in web search function ensures that the answers are not limited to the model's training data and include the latest developments.
Flexible customization: You can freely combine any AI models for which you have API keys to create a customized research team.
Limitations
Requires multiple API keys: To use models from different providers, you need to apply for and configure their API keys separately.
Potentially higher cost: Invoking multiple models simultaneously may consume more API quotas than using only one model.
Dependent on the client: It must be used through a client that supports MCP (such as Claude Desktop) and cannot run independently.
Configuration requires technical knowledge: Although it is simple to use, the initial configuration requires editing a JSON configuration file, which may pose a slight barrier to non-technical users.
How to use
Prepare API keys
According to the AI models you want to use, go to the corresponding official websites (such as OpenAI, Anthropic, Google AI Studio, OpenRouter, etc.) to register and obtain API keys.
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Configure environment variables
Set the API keys obtained in the previous step as system environment variables. Usually, you add an `export` statement to your user configuration file (such as ~/.bashrc or ~/.zshrc), and then restart the terminal or run the `source` command.
Configure the MCP client
Open the MCP configuration file of the MCP client you are using (such as Cursor). For Cursor, the file path is usually `~/.cursor/mcp.json`. If the file does not exist, create one.
Add ThinkingCap server configuration
In the MCP configuration file, add the ThinkingCap configuration in the specified format. In the `args` array, list the combination of models you want to use.
Restart the client and start using
Save the configuration file, and then completely restart your MCP client (such as Cursor or Claude Desktop). After restarting, you can ask your AI assistant questions as usual, and it will automatically invoke ThinkingCap in the background for multi-model research.
Usage examples
In-depth technical research
You want to learn about 'the latest developments in quantum computing' but are not sure which AI has the most comprehensive information. Through ThinkingCap, you can simultaneously ask GPT-4, Claude 3.5 Sonnet, and Gemini 2.0. They will search from different sources and synthesize the information to provide you with a report covering multiple aspects such as hardware, algorithms, and company developments.
Multi-angle content creation
You need to write a promotional copy for a new product and hope to integrate creative ideas in different styles. You can configure a ThinkingCap team that includes Claude, which is 'good at storytelling', and GPT-4o, which is 'good at grabbing attention', and let them brainstorm together.
Fact-checking and learning
You see a controversial statement online (such as 'Eating coconut oil can significantly prevent Alzheimer's disease') and want to quickly understand the scientific consensus. Let multiple AI'researchers' in ThinkingCap consult the latest research papers and authoritative medical websites.
Frequently asked questions
Is ThinkingCap free?
Do I need to configure API keys for each model?
Will the web search function consume my traffic or incur additional fees?
Can I use only one model?
How can I know if the client I'm using supports MCP?
What should I do if there is no response after configuration?
Related resources
Model Context Protocol official website
Understand the official introduction, technical specifications, and list of supported clients of the MCP protocol.
ThinkingCap GitHub repository
Source code, issue feedback, and latest updates of the ThinkingCap project.
OpenRouter model marketplace
View all AI models available through OpenRouter, their rankings, and pricing.
Cursor official documentation - MCP
How to configure and use the MCP server in Cursor IDE.
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