Multi-Agent Career Mentor Built with Ollama and MCP for Local AI

A developer has built a multi-agent AI system called "AI Career Mentor" that reads resumes and produces comprehensive career intelligence reports. The system runs fully locally using Ollama with llama3, requiring no API keys or external costs.

Technical Architecture

The system consists of five specialized agents that chain their outputs:

• Each agent receives the previous agent's output as shared context
• The roadmap agent knows your skill gaps from the analysis agent
• The salary agent knows your roadmap from the previous agent
• This chaining makes the report progressively smarter as it moves through the pipeline

Tech Stack Details

• AI Engine: Ollama + llama3 (100% local)
• RAG System: FAISS + SentenceTransformers for indexing your knowledge base
• Tool Layer: MCP (Model Context Protocol) - FastAPI spawns the MCP server as a subprocess and communicates via stdio JSON-RPC
• Resume Processing: pdfplumber for reading PDF resumes
• Frontend: React

MCP Implementation Notes

The developer found MCP particularly interesting to build. MCP is Anthropic's open standard for connecting AI to tools, using one server that can work with any client. The system also connects to Claude Desktop via configuration file, allowing Claude to call all 9 tools directly.

A notable bug encountered: MCP SDK v1.x changed handler signatures completely. Old code passes a full request object, while new code unpacks name and arguments directly. This caused significant debugging time.

Report Outputs

The system generates a complete career intelligence report including:

• Resume analysis
• Skill gap identification
• 6-month roadmap
• Salary strategy
• Interview preparation

All components run in one shot after processing the resume.

Resources

The project is available on GitHub with a video walkthrough. The developer notes that the RAG setup and MCP client/server wiring were the trickiest parts to implement.