Project-R

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Inspiration

The idea behind MCP-Hack was born out of a growing demand for an integrated platform that could supercharge deep research, intelligent automation, and data-driven decision-making—all under one roof. We imagined a system that not only automates repetitive tasks but also delivers actionable insights and handles professional outreach with precision. The goal? Free up valuable time, amplify productivity, and empower professionals with AI at their fingertips.

What We Learned

Throughout the development of MCP-Hack, we uncovered key insights:

• The Power of Fusion: Merging multiple AI ecosystems—CrewAI, OpenAI, and Anthropic—with modern web technologies like Next.js and TailwindCSS unlocked seamless, responsive user experiences.
• Iteration Through Data: Leveraging Weights & Biases enabled robust experiment tracking and performance analytics, driving continuous iteration and improvement.
• AI + Automation = Magic: Integrating AI-driven workflows with Playwright and Mastra opened new frontiers for automating professional tasks, from research to outreach.

How We Built MCP-Hack

Architecture Overview

MCP-Hack is a modular platform built on three core components:

• MCP-CrewLink (Python): An AI research assistant built on CrewAI and MCP servers that performs web searches, analyzes content, and generates comprehensive reports.
• nextjs-mcp-client (Next.js/React): A sleek, interactive web interface for managing research, visualizing results, and interfacing with AI tools.
• Agent-mastra (TypeScript): A smart LinkedIn automation tool powered by Playwright and Mastra, driven by context-aware agents.

Integration Strategy

We utilized environment variables and the Model Context Protocol to securely and flexibly link all services. This modular design allowed each component to evolve independently while maintaining tight integration.

Experimentation Workflow

Every research task and automation run was tracked using Weights & Biases, giving us granular visibility into system performance and behavior. This enabled quick feedback loops and better decision-making throughout development.

Challenges We Faced

• Cross-Stack Communication: Bridging Python (CrewAI), Node.js (Next.js), and TypeScript (Mastra) demanded precise interface design and a consistent communication protocol.
• API Rate Limits & Compliance: We navigated limitations—especially with LinkedIn and web search APIs—while staying compliant with terms of service.
• Automation Safety: Implementing safety features such as rate limiting, review modes, and persistent memory was critical to ensuring ethical and reliable automation.
• UX for Complexity: Building an interface that is both powerful and easy to use—especially for intricate workflows—required multiple rounds of iteration and direct user feedback.

Math Meets AI

We embedded advanced AI techniques into the core of the platform. For instance, to determine the relevance of a document (D) to a user’s query (Q), we compute a similarity score (S) using vector embeddings:

$$ S(Q, D) = \text{cosine_similarity}(\text{embedding}(Q), \text{embedding}(D)) $$

This scoring system ensures users always receive the most contextually relevant information—fast, accurate, and grounded in math.

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Built With

• crew
• crewai
• exa
• nextjs
• node.js
• wandb