This is a submission for the Algolia MCP Server Challenge
What I Built
An AI-powered learning dashboard with a React/Vite frontend and a Flask backend. Users can upload PDFs or submit web links - the backend processes will extract text (using pdfplumber for PDFs and Jina Reader for web links), then enrich with AI-generated summaries and key points via OpenAI. All enriched data and metadata are indexed in the Algolia MCP Server, enabling fast, unified, and semantic search across all resources. The system also securely manages user authentication with AWS, allowing users to search, review, and download their learning materials with ease.
Demo
Deployed Link: https://study-documents-fe.vercel.app/login
*Github Repo: *
Frontend: https://github.com/sakshi30/study_documents_fe
Backend: https://github.com/sakshi30/study-enhancement-bknd
Demo:
https://drive.google.com/file/d/1AhO3UQ-9s43K_jO6AXwx7yfeQRU9HRJb/view?usp=sharing
Screenshots:
How I Utilized the Algolia MCP Server
I utilized the Algolia MCP Server as the central indexing and retrieval layer for all the learning materials my users upload, including PDFs and web links. By sending AI-enriched summaries and metadata to MCP, I enable fast, semantic search across diverse content sources. This integration greatly simplifies how my platform organizes and delivers intelligent, relevant information to users instantly.
Key Takeaways
Development Process:
I started by designing a modular backend with Flask, integrating pdfplumber for PDF text extraction and Jina Reader for web link content parsing. I added support for user authentication with AWS RDS to ensure secure uploads. For each resource, the backend used OpenAI to generate structured summaries and key points in JSON. All enriched metadata and download links were indexed into the Algolia MCP Server, which powered the frontend’s unified and semantic search experience built with React and Vite.
Challenges Faced:
One major challenge was handling diverse input formats—extracting clean, useful text from PDFs (which can be poorly formatted) and ensuring reliable content parsing from web links, given varied site structures. Integrating multiple external APIs (AWS, OpenAI, Jina, and Algolia MCP) required careful error handling and thoughtful workflow design, especially for asynchronous processing and returning accurate status to users. If I had to improve I would have used Cognito for authentication and stored the pdfs in S3.
What I Learned:
Through this project, I learned effective strategies for building robust, API-driven backend pipelines that leverage multiple third-party services. I gained hands-on experience integrating advanced AI (summarization, key points) and using Algolia MCP Server to create a scalable, interoperable search layer. Most importantly, I saw the value of modular, service-oriented architecture: it made troubleshooting easier, future expansion straightforward, and gave end users a seamless, intelligent learning experience.
Sakshi Srivastava
Dev.to: https://dev.to/sakshi_srivastava
Linkedin: https://www.linkedin.com/in/srivassa/
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