LeetCode Crawler

LeetCode Discussion Crawler + MCP Server

Crawl LeetCode interview discussion forums, extract structured intelligence with Claude AI, and expose the data as an MCP (Model Context Protocol) server for Claude Desktop and Claude Code.

What it does

• Crawls LeetCode discussion forums filtered by company tag (google, meta, amazon, etc.)
• Classifies posts by type (leetcode, discuss_link, discussion)
• Groups and deduplicates discussions by post title + company across pages
• Optionally enriches posts with AI-extracted fields: problem_name, leetcode_url, interview_stage, difficulty, reported_outcome
• Saves to CSV (per-company cache or monthly output)
• Exposes 4 tools over stdio MCP transport for use with Claude Desktop / Claude Code

Installation

git clone https://github.com/louisfghbvc/mcp-leetcode-crawler.git
cd mcp-leetcode-crawler
pip install -r requirements.txt

Usage

CLI

# Crawl 10 pages of Google interview discussions
python cli.py --company google --pages 10

# Filter to posts from the last 30 days
python cli.py --company google --pages 10 --since 30d

# Crawl and enrich with AI extraction (requires ANTHROPIC_API_KEY)
python cli.py --company google --pages 10 --enrich

# Other companies
python cli.py --company meta --pages 5 --since 7d

Available CLI options:

• --company: Company tag (default: google)
• --pages: Pages to crawl, 15 posts/page (default: 10)
• --output: Output CSV path (default: leetcode_interview_questions.csv)
• --output-dir: Directory for monthly CSVs (default: output)
• --since: Time window filter — Nd (days) or Nw (weeks), e.g. --since 14d
• --enrich: AI-extract structured fields from each post (requires ANTHROPIC_API_KEY)
• --verbose: Debug logging
• --category: Discussion category (default: interview-question)

MCP Server

Register with Claude Desktop or Claude Code:

# Run directly
python mcp_server.py

# Or register with Claude Desktop
mcp install mcp_server.py

The server exposes 4 tools:

Tool	Description
search_discussions(company, days)	Filter cached posts by company and time window
get_hot_problems(company, limit)	Top LeetCode problems ranked by discussion frequency
get_thread(post_id)	Real-time single-thread fetch (no cache)
refresh(company, num_pages, days, enrich)	Crawl and rebuild on-disk cache

Cache lives in ./cache/{company}_questions.csv. Run refresh once to populate before using search_discussions or get_hot_problems.

AI Enrichment

When --enrich (CLI) or enrich=True (MCP refresh) is used, each post gets 5 new fields:

Field	Values
problem_name	LeetCode problem name, or null
leetcode_url	Full problem URL, or null
interview_stage	phone screen / online assessment / onsite / final round / null
difficulty	easy / medium / hard / null
reported_outcome	passed / failed / no mention / null (null = extraction failed, will retry)

Cost: ~$0.001/post with claude-haiku-4-5-20251001. Already-enriched posts are skipped on re-run.

Project Structure

mcp-leetcode-crawler/
├── crawler.py          # Core crawler: fetch, deduplicate, filter, classify posts
├── extractor.py        # AI extraction layer: Claude Haiku → structured fields
├── mcp_server.py       # MCP server: 4 tools over stdio transport
├── cli.py              # Command-line interface
├── requirements.txt    # Python dependencies
├── test_crawler.py     # Crawler unit tests
├── test_mcp_server.py  # MCP server unit tests (22 tests)
├── test_extractor.py   # Extractor unit tests (12 tests)
├── test_cli.py         # CLI integration tests (2 tests)
└── cache/              # Per-company CSV cache (git-ignored)

Requirements

• Python 3.12+
• ANTHROPIC_API_KEY env var (only for --enrich)

License

MIT