Blinkly
AI-powered deep research tool with Gemini LLMs, web scraping, and comprehensive report generation.
Deep-research is an AI-powered research assistant that leverages Model Context Protocol (MCP) to provide iterative, deep research capabilities. This server enables seamless integration with various AI applications such as Claude Desktop, Continue, Cursor, and more through a standardized protocol. By combining web scraping using Firecrawl and Gemini Language Models for advanced query generation and report generation, Deep-research offers an efficient and comprehensive solution for conducting research.
Deep-research is designed to integrate effortlessly with various AI clients such as Claude Desktop, Continue, Cursor, and others. This integration allows these applications to connect and utilize the server's capabilities through a standardized protocol, ensuring interoperability and ease of use.
The tool supports an iterative research process where it generates search queries based on initial inputs, processes results, and dives deeper into relevant topics until the desired depth or breadth is achieved. This iterative approach ensures that the research remains focused and relevant, leading to more accurate and comprehensive findings.
Deep-research uses advanced language models from the Gemini API to generate targeted search queries. These queries are designed to be smart, efficient, and aligned with specific research goals, providing a robust starting point for exploration.
Users can control the depth and breadth of their research through configurable parameters. This flexibility allows researchers to tailor their investigation to suit various scenarios, whether it's a broad overview or an in-depth analysis on a particular topic.
The system intelligently generates follow-up questions that help refine the initial query, ensuring that the subsequent searches are more focused and effective. These questions also provide strategic direction for the next phase of the research process.
Deep-research produces detailed, ready-to-use markdown reports that consolidate findings and sources efficiently. This feature ensures that researchers can quickly document their work and present their results effectively.
To maximize efficiency, Deep-research employs concurrent processing techniques to handle multiple searches and result analyses simultaneously, ensuring faster turnaround times for research tasks.
Deep-research is built to be a seamless Model Context Protocol tool. This means it can work directly with any AI application that supports MCP, providing a consistent and interoperable framework for conducting deep research across different platforms.
The integration process involves setting up the necessary environment variables and dependencies before running the server or invoking its functions via API calls from MCP-compatible clients.
Using Firecrawl as a backend for web data extraction, Deep-research can efficiently gather and organize information from various sources on the internet. This feature is critical for broadening the scope of research and ensuring that the findings are well-supported by diverse sources.
Gemini LLMs play a crucial role in generating smart queries and processing results. These models are fine-tuned to handle complex language tasks, making them ideal for query refinement and content analysis within the research process.
To get started with Deep-research, you need to have a Node.js environment setup (v22.x recommended). You can download it from Node.js website.
Clone the Repository:
git clone [your-repo-link-here]
Install Dependencies:
npm install
Configure Environment Variables:
Set up the necessary environment variables in your .env file or directly in the script if you prefer.
Run the Server:
npm start
npm run start "your research query"
Academics and researchers can leverage Deep-research to conduct thorough investigations on specific topics. By integrating with scholarly databases and web scraping capabilities, it helps gather relevant data and produce compelling research papers.
Business professionals can use Deep-research for market analysis and industry reports. The tool’s ability to generate smart queries and extract information from diverse sources makes it an ideal choice for compiling comprehensive analyses.
MCP clients such as Claude Desktop, Continue, Cursor are fully compatible with Deep-research. They can seamlessly connect to the server through defined API endpoints or command-line invocations.
| MCP Client | Resources | Tools | Prompts | Status |
|---|---|---|---|---|
| Claude Desktop | ✅ | ✅ | ✅ | Full Support |
| Continue | ✅ | ✅ | ✅ | Full Support |
| Cursor | ❌ (Only Tool Access) | ✅ | ❌ | Tools Only |
These performance metrics highlight the effectiveness of Deep-research in enhancing overall research productivity and reducing resource consumption.
You can configure various environment variables to customize how Deep-research operates, including API keys, data sources, and other settings. Example configurations include:
{
"mcpServers": {
"[server-name]": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-[name]"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
Security is paramount in AI applications. Deep-research implements several security measures, such as secure API endpoints, encryption of sensitive data, and regular code audits to ensure robust protection against potential threats.
Q: How do I integrate Deep-research with my AI application? A: You can integrate Deep-research with your AI application by configuring the MCP protocol endpoint in your client settings. Refer to the compatibility matrix for specific details on supported clients.
Q: What are the most important features of Deep-research? A: Key features include iterative research processes, smart query generation using Gemini models, and comprehensive markdown reporting capabilities. These ensure efficient and accurate data collection and documentation.
Q: Can I use Deep-research for commercial purposes? A: Yes, using the MIT license, you can freely utilize Deep-research in both personal and commercial projects without any restrictions on redistribution or modification. However, always follow best practices when integrating AI services into your applications.
Q: How does Deep-research handle data privacy? A: Data privacy is managed through secure API endpoints, encryption of sensitive information during transmission, and compliance with relevant regulations like GDPR/CCPA to protect user data integrity and confidentiality.
Q: Is there a community of developers building around MCP servers like Deep-research? A: Yes, the MCP ecosystem includes numerous tools and resources that complement each other. Engage with the community through forums, GitHub issues, or relevant tech events for support and collaboration opportunities.
Deep-research welcomes contributions from developers who wish to enhance its capabilities or address issues in the codebase. To contribute, follow these steps:
Fork the Repository: Go to the official Deep-research repository on GitHub and fork it to your own account.
Set Up Your Development Environment: Clone the forked repository onto your local machine and ensure that Node.js is installed properly.
Development Guidelines:
Testing & Deployment:
Documentation: Update documentation as necessary to reflect new features or improvements in the codebase.
MCP servers like Deep-research form part of a broader ecosystem designed to facilitate interoperable AI integrations. Resources include:
By leveraging this ecosystem, developers can create robust AI applications that benefit from enhanced functionality provided through MCP integration.
graph TD
A[AI Application] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server]
C --> D[Data Source/Tool]
style A fill:#e1f5fe
style C fill:#f3e5f5
style D fill:#e8f5e8
graph LR
A[API Endpoint] -- POST --> B[MCP Protocol Layer]
B -- Request Handling --> C[Server Logic]
C -- Data Processing --> D[Storage/Data Sources]
D -- Responses --> E[MCP Client]
style A fill:#b6e8fe
style C fill:#d2e9da
style D fill:#f0e4c7
Scenario: An academic researcher needs to gather literature on the impact of chatbot technologies in education.
Scenario: A business analyst wants to conduct a comprehensive market analysis on the latest trends in artificial intelligence development tools.
{
"mcpServers": {
"deepResearchServer": {
"command": "npx",
"args": ["@modelcontextprotocol/server-deepresearch"],
"env": {
"API_KEY": "your_api_key_here"
}
}
}
}
Deep-research serves as a powerful tool for conducting deep and iterative research within the Model Context Protocol framework. Its capabilities align perfectly with various AI application needs, offering robust features to support diverse workflows while ensuring optimal performance and security.
By participating in this open-source community or adopting Deep-research for your projects, you can harness its full potential and contribute to advancing the state of AI integration and research methodologies.
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