MCP Protocol-Driven AI Research Assistant: Open-Source Practice for Real-Time Search and Professional Report Generation

The GitHub open-source project ai-intern-final-ashutosh-singh is an AI research tool based on FastAPI and MCP protocol, designed to solve the problem of researchers quickly filtering valuable content and converting it into structured reports amid information explosion. Core features include real-time web and academic search, large-model synthetic analysis, professional PDF report export, streaming response to enhance user experience, and multi-model failover to ensure system reliability.

In the era of information explosion, researchers and knowledge workers face the challenge of filtering massive information and producing structured knowledge. This project addresses this pain point by providing a one-stop AI-driven research tool that integrates search, intelligent analysis, and report generation capabilities to help users process information efficiently.

1. MCP Protocol Integration: Through the MCP client/runner/search server architecture, it isolates the tool execution environment from the FastAPI event loop and automatically falls back to direct search upon failure;
2. Dual-Model Chained Calling: Prioritizes using OpenRouter's GPT model, and falls back to Groq's LLaMA model (70B→8B) upon failure, balancing reliability and cost;
3. Real-Time Streaming Response: Uses SSE technology to push results token by token, combined with a dark glassmorphism UI and progress indicator;
4. Parallel Academic Search: Simultaneously retrieves from four major databases: arXiv, PubMed, Semantic Scholar, Google Scholar, combined with web search;
5. PDF Export Engine: Generates standardized reports based on fpdf2, supporting formula rendering, clickable hyperlinks, etc.

The project adopts a layered architecture with clear code structure:

• backend: agent.py (AI orchestration), config.py (Pydantic configuration), exporter.py (export), main.py (FastAPI entry), mcp_client/runner.py (MCP communication), etc.;
• frontend: index.html (application shell), style.css (UI design), app.js (SSE client);
• mcp_servers: web_search_server.py (web search), filesystem_server.py (file system). It follows the principle of separation of concerns, with centralized configuration management and no hard-coded credentials.

Deployment steps: 1. Clone the repository and create a virtual environment; 2. Install dependencies (pip install -r requirements.txt); 3. Copy .env.example to .env and configure API keys; 4. Start the service (uvicorn backend.main:app --reload --port 8000). Main API endpoints: GET /research (streaming research results), POST /export (export PDF/TXT reports), GET /health (service health check).

Applicable to: 1. Academic research (quickly generate literature reviews and reports); 2. Market research (collect and analyze industry information); 3. Content creation (assist in writing in-depth articles); 4. Knowledge management (integrate scattered information into structured documents). Helps users improve information processing efficiency and reduce filtering costs.

This project integrates MCP protocol, large language models, and modern Web technologies to implement features such as streaming architecture, multi-model failover, and academic search integration, making it an excellent open-source case in the field of AI-assisted research. Its code organization and engineering practices (type hints, docstrings, centralized configuration, error handling) have high reference value for developers.