Build a Multi-Agent LLM System with Model Context Protocol (MCP) for Research Summarization and Blog Generation

Project Description:
Build a multi-agent LLM system that uses a custom-defined Model Context Protocol (MCP) to manage collaboration between agents for a complex text generation pipeline.

Project Objective:
To automate the end-to-end process of:
1. Researching latest academic papers on a given topic
2. Summarizing the content while managing token limits and contextual relevance,
3. Planning a blog structure,
4. Writing a full-length blog post.

Each step is handled by a separate AI agent, coordinated via a custom MCP that preserves task history, memory, context, and agent-specific instructions.

You’ll define and implement an MCP JSON schema to structure and exchange context across agents. Each agent will:
• Receive agent_role, task_id, goals, memory, history, token_budget, tools, etc.
• Process context and produce structured output that can be passed to the next agent.
• Update a shared memory store (e.g., ChromaDB or local JSON DB).

Deliverables:
1. Working prototype with all 4 agents implemented and collaborating through MCP
2. MCP documentation (your schema design, agent behavior expectations)
3. Ability to input a topic and generate a complete blog post in 3–5 minutes
4. Streamlit or React UI to show agent outputs at each step