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Miriam-Epstein

career-navigator-mcp

by Miriam-Epstein
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career-navigator-mcp

An MCP (Model Context Protocol) server that exposes the career diagnosis AI system as callable tools — powered by Google Gemini and a RAG knowledge base of 10 professions.

Any MCP-compatible client (Claude Desktop, Cursor, etc.) can integrate this server and call its tools directly.

Tools

analyze_skills

Analyzes free text describing a user and returns a list of skills with match percentages.

Input:

{ "text": "I enjoy working with people and solving complex problems..." }

Output:

[
  { "skill": "Interpersonal Communication", "match_percentage": 95 },
  { "skill": "Problem Solving",             "match_percentage": 85 }
]

recommend_profession

Receives a skills array and recommends the most suitable profession using RAG — matched against a real knowledge base of 10 professions.

Input:

{
  "skills": [
    { "skill": "Interpersonal Communication", "match_percentage": 95 },
    { "skill": "Problem Solving",             "match_percentage": 85 }
  ]
}

Output:

[
  {
    "profession": "מנהל משאבי אנוש",
    "match_percentage": 91,
    "explanation": "..."
  }
]

Related MCP server: MCP Jobs Recommender Agent

Architecture

MCP Client (Claude Desktop / Cursor / any AI agent)
    │
    │  tools/call: analyze_skills / recommend_profession
    ▼
career-navigator-mcp (StdioServerTransport)
    │
    ├── analyze_skills ──► Gemini API (structured JSON output)
    │
    └── recommend_profession ──► RAG: professions_data.json
                                      └── top 3 matches injected into Gemini prompt

Setup

Prerequisites

Install

git clone https://github.com/Miriam-Epstein/career-navigator-mcp.git
cd career-navigator-mcp
npm install

Environment

Create a .env file in the project root:

GEMINI_API_KEY=your_key_here

Connecting to Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "career-navigator": {
      "command": "node",
      "args": ["/absolute/path/to/career-navigator-mcp/index.js"]
    }
  }
}

Model Fallback

Every Gemini call uses an automatic fallback chain:

gemini-2.5-flash-lite  →  (on 429 / quota error)  →  gemini-2.5-flash

Tech Stack

Layer

Technology

Protocol

MCP SDK (@modelcontextprotocol/sdk)

Transport

StdioServerTransport

AI

Google Gemini (@google/genai)

RAG

Local JSON knowledge base (10 professions)

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

No tool schema history has been recorded yet.

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