WHED Tools — Higher Education Intelligence Pipeline

An MCP-native pipeline for collecting structured intelligence on higher education institutions, aligned with the IAU World Higher Education Database (WHED) schema.

Scrape → Extract → Validate → Save — the Host LLM performs extraction directly using MCP tools. No external LLM required.

Built on samirsaci/mcp-webscraper.

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Overview

Step	How
Scrape	MCP crawl_website or standalone run_scraper.py — schema-driven crawl, PDF extraction
Extract	Host LLM reads scraped content, uses get_extraction_schema + get_db_context
Validate	validate_profile — Pydantic schema + WHED DB picklist checks
Save	save_profile — write to output/structured/

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Architecture

┌─────────────────────────────────────────────────────────────────┐
│  HOST LLM  (Claude in Cursor / any MCP client)                  │
│                                                                 │
│  crawl_website(url)      →  get_extraction_schema()             │
│  scrape_url(url)             get_db_context(domain)            │
│                                     │                           │
│  Host LLM reads content and fills JSON                          │
│                                     │                           │
│  validate_profile(json)  →  save_profile(domain, json)           │
└─────────────────────────────────────────────────────────────────┘
         │                      │                       │
         ▼                      ▼                       ▼
   output/pages/           schema.py              output/structured/
   output/sites/           db_reference.py

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Project Structure

mcp-webscraper/
├── MCP_server/
│   ├── server.py           # MCP entry — 9 tools (scrape + extraction)
│   ├── models/
│   └── utils/
│       └── web_scraper.py  # Scraper (static, Playwright, pdfplumber)
├── schema.py               # SchoolProfile, EXTRACTION_TEMPLATE, FIELD_URL_HINTS
├── db_reference.py         # WHED DB — picklists, reference examples, ground truth
├── run_scraper.py          # Standalone CLI — schema-driven crawl, PDF extraction
├── docs/
│   ├── USAGE_GUIDE.md      # Architecture, flow, outputs, comparison
│   ├── PROJECT_ITERATIONS.md
│   └── MCP_VS_N8N_COMPARISON.md
└── output/
    ├── pages/              # Per-page cache from crawl
    ├── sites/              # Combined site crawl
    ├── structured/         # MCP extraction output
    ├── ground_truth/       # WHED DB exports
    └── stages/             # Human review staging

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Prerequisites

• Python 3.10+
• uv package manager
• Cursor (for MCP usage)
• MySQL with WHED database (optional — for DB grounding and comparison)

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Installation

git clone https://github.com/your-username/mcp-webscraper.git
cd mcp-webscraper
uv sync
uv run playwright install chromium

Copy .env.example to .env and add WHED DB credentials (if available).

Connect MCP to Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "whed-tools": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/mcp-webscraper",
        "python",
        "MCP_server/server.py"
      ]
    }
  }
}

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MCP Tools (whed-tools)

Tool	Description
scrape_url	Fetch HTML from a URL
extract_data	Extract by CSS selector
extract_first	First matching element
batch_scrape	Multiple URLs
crawl_website	Discover and crawl site (schema_filter=True to skip irrelevant pages)
extract_pdf_text	Download a PDF and extract its text content
get_extraction_schema	WHED field template (REQUIRED only)
get_db_context	Picklists + reference example for domain
validate_profile	Pydantic + DB picklist validation
save_profile	Save profile to output/structured/

Example prompt

"Crawl https://www.example.edu and extract a WHED profile. Use get_extraction_schema and get_db_context, then validate and save."

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Standalone Scripts

Scrape (schema-driven, with PDFs)

Edit run_scraper.py (TARGET_URL, MODE, etc.), then:

uv run python run_scraper.py

• Uses schema.FIELD_URL_HINTS to follow only relevant URLs
• Extracts text from PDFs via pdfplumber

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Schema & DB Grounding

• REQUIRED fields are in EXTRACTION_TEMPLATE; DEFERRED fields are in Pydantic but not prompted.
• With WHED DB: picklists, few-shot examples, and post-validation reduce hallucination.
• Edit schema.py to add or reactivate fields.

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Documentation

Doc	Content
USAGE_GUIDE.md	Architecture, flow, and outputs
PROJECT_ITERATIONS.md	Evolution from Ollama to MCP-native
MCP_VS_N8N_COMPARISON.md	KPI comparison with N8N + Firecrawl

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License

MIT — based on samirsaci/mcp-webscraper.