Bengaluru AI Job Radar

Bengaluru AI Job Radar is a Python FastMCP server that helps an AI agent search for AI internship and early-career AI roles in Bengaluru, save them to a local JSON job tracker, and render a Prefab dashboard UI.

Built with FastMCP 3.4.x, Prefab UI 0.20.x, Tavily for internet search, and JSON for local persistence.

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Assignment Mapping

Assignment Requirement	Implementation
Custom MCP server	Python FastMCP server (server.py)
Internet-related function	search_ai_jobs uses Tavily API
Local file CRUD	job_tracker_db performs CRUD on local JSON file (data/bengaluru_ai_job_radar.json)
UI communication	render_job_dashboard returns FastMCP Prefab UI components
Web app / dashboard	Prefab-rendered dashboard inside MCP-compatible host
Prompt forcing all 3 tools	Included in demo_prompt.md

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Architecture

User prompt
  → Agent calls search_ai_jobs (Tavily internet search)
  → Agent saves results via job_tracker_db (JSON CRUD)
  → Agent reads records via job_tracker_db (JSON CRUD)
  → Agent calls render_job_dashboard (Prefab UI)
  → Prefab dashboard appears in MCP host

MCP Tools

Tool	Purpose	Category
search_ai_jobs	Search Tavily for AI/ML/GenAI internships and junior roles in Bengaluru	Internet
job_tracker_db	Create, read, update, delete, and manage job leads in a local JSON database	Local CRUD
render_job_dashboard	Render a rich Prefab UI dashboard with summary metrics, job table, and charts	UI

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Setup (Windows)

cd C:\Cursor\EAGv3\S4
cd bengaluru-ai-job-radar

python -m venv .venv
.venv\Scripts\activate

pip install -e ".[dev]"

copy .env.example .env
# Edit .env and add your TAVILY_API_KEY

Setup (macOS / Linux)

cd /path/to/bengaluru-ai-job-radar

python3 -m venv .venv
source .venv/bin/activate

pip install -e ".[dev]"

cp .env.example .env
# Edit .env and add your TAVILY_API_KEY

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Environment Variables

Create a .env file (or set system environment variables):

TAVILY_API_KEY=tvly-xxxxxxxxxxxxxxxxx
DATABASE_PATH=data/bengaluru_ai_job_radar.db

• TAVILY_API_KEY (required): Get one free at tavily.com.
• DATABASE_PATH (optional): Defaults to data/bengaluru_ai_job_radar.db relative to the project root.

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Running the MCP Server

Direct Python execution

python -m bengaluru_ai_job_radar.server

Using FastMCP CLI

fastmcp run src/bengaluru_ai_job_radar/server.py

App preview (if supported)

fastmcp dev src/bengaluru_ai_job_radar/server.py

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Connecting to an MCP Host

Add this to your MCP host configuration (Claude Desktop, Cursor, VS Code, etc.):

{
  "mcpServers": {
    "bengaluru-ai-job-radar": {
      "command": "python",
      "args": ["-m", "bengaluru_ai_job_radar.server"],
      "cwd": "C:\\Cursor\\EAGv3\\S4\\bengaluru-ai-job-radar",
      "env": {
        "TAVILY_API_KEY": "your_key_here",
        "DATABASE_PATH": "data/bengaluru_ai_job_radar.db"
      }
    }
  }
}

Note: Exact MCP host configuration may differ depending on Claude Desktop, Cursor, VS Code, ChatGPT MCP Apps, or another host.

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Demo Prompt

Copy this prompt into your MCP-connected AI agent to exercise all 3 tools:

Use the Bengaluru AI Job Radar MCP server to complete this full workflow.

First, use the MCP internet search tool search_ai_jobs to find companies currently hiring AI Interns, ML Interns, GenAI Interns, LLM Engineer Interns, AI Implementation Engineer Interns, or Junior AI Engineers in Bengaluru.

Prioritize roles involving Python, LLMs, RAG, AI agents, embeddings, prompt engineering, fine-tuning, model training, NLP, or AI backend development.

Save at least 5 relevant job leads to the local JSON job tracker using the MCP CRUD tool job_tracker_db.

Then read the saved records back using job_tracker_db with the list_jobs operation.

Finally, render the saved results using the FastMCP Prefab UI tool render_job_dashboard.

Do not answer from memory. Do not skip any step. You must call all 3 tools:

1. search_ai_jobs
2. job_tracker_db
3. render_job_dashboard

The full prompt is also available in demo_prompt.md.

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Example Workflow

1. Search for roles

The agent calls search_ai_jobs with:

{
  "role_query": "AI Intern",
  "location": "Bengaluru",
  "max_results": 10
}

2. Save job leads

The agent calls job_tracker_db for each result:

{
  "operation": "create_job",
  "payload": {
    "company": "Sarvam AI",
    "role_title": "AI Intern",
    "role_type": "internship",
    "location": "Bengaluru",
    "skills": ["Python", "LLM", "RAG"],
    "fit_score": 85,
    "source_platform": "LinkedIn",
    "source_url": "https://linkedin.com/jobs/view/123"
  }
}

3. List saved jobs

{
  "operation": "list_jobs",
  "payload": {"min_fit_score": 50}
}

4. Update a role status

{
  "operation": "update_status",
  "payload": {
    "job_id": "...",
    "new_status": "applied",
    "event_note": "Applied via company careers page"
  }
}

5. Add a note

{
  "operation": "add_note",
  "payload": {
    "job_id": "...",
    "note": "Reach out to founder on LinkedIn"
  }
}

6. Render dashboard

The agent calls render_job_dashboard → a Prefab UI dashboard appears with summary cards, a job table, recent activity, and skill frequency.

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Running Tests

pytest tests/ -v

Tests cover:

• Fit score calculator — scoring rubric, caps, keyword boosts
• Normalization — company names, slug IDs, role type inference, skill extraction
• JSON store — full CRUD lifecycle, upsert, deduplication, dashboard aggregation

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Project Structure

bengaluru-ai-job-radar/
  README.md
  pyproject.toml
  .env.example
  .gitignore
  demo_prompt.md

  src/
    bengaluru_ai_job_radar/
      __init__.py
      server.py          # FastMCP server entrypoint
      config.py           # Environment variable loading
      schemas.py          # Pydantic validation models

      tools/
        __init__.py
        search.py          # search_ai_jobs MCP tool
        database.py        # job_tracker_db MCP tool
        dashboard.py       # render_job_dashboard MCP tool (Prefab UI)

      services/
        __init__.py
        tavily_service.py  # Tavily API integration
        fit_score.py       # Deterministic fit-score calculator
        normalization.py   # Company/role normalization, skill extraction

      storage/
        __init__.py
        json_store.py      # JSON CRUD store (JobRadarStore)

  data/
    .gitkeep              # JSON DB created here at runtime

  tests/
    test_fit_score.py
    test_normalization.py
    test_json_store.py

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Known Limitations

• Tavily search result quality depends on public indexing of job boards.
• Some job platforms may block direct scraping, so the tool relies on Tavily snippets and source links.
• Compensation data may be unavailable for many listings — shown as "unknown".
• Prefab UI APIs are actively evolving; dependency pinning to prefab-ui>=0.20.0,<1.0.0 is used.
• Dashboard interactivity depends on the MCP host's support for FastMCP Apps / Prefab rendering.
• Company name extraction from search results is best-effort; some may show as "unknown".
• The fit-score algorithm is deterministic and rule-based — it does not use ML or LLM reasoning.

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Dependencies

Package	Purpose
fastmcp[apps] ≥3.4.0	MCP server framework + Prefab app support
prefab-ui ≥0.20.0	Prefab UI components (Card, DataTable, Badge, etc.)
pydantic ≥2.0.0	Request/response validation
python-dotenv ≥1.0.0	.env file loading
httpx ≥0.27.0	HTTP client (Tavily fallback)
tavily-python ≥0.5.0	Tavily search SDK
rich ≥13.0.0	Rich terminal output

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License

MIT