LeetCode Crawler MCP Server

@louisfghbvc

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基本情報

カテゴリ

ブラウザ自動化

トランスポート

stdio

作者

louisfghbvc

LeetCode Crawler MCP Server について

mcp server for craw the interview questions in discussion

#crawler #leetcode

LeetCode Crawler MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-leetcode-crawler": {
      "command": "python",
      "args": [
        "cli.py",
        "--company",
        "google",
        "--pages",
        "10"
      ]
    }
  }
}

LeetCode Crawler MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is LeetCode Crawler?

LeetCode Crawler is an MCP server that crawls LeetCode interview discussion forums filtered by company tag, optionally enriches posts with Claude AI to extract structured fields, and exposes the data via four stdio-based tools for Claude Desktop and Claude Code.

How to use LeetCode Crawler?

Install by cloning the repository and running pip install -r requirements.txt. Use the CLI (e.g., python cli.py --company google --pages 10 --enrich) or run the MCP server directly (python mcp_server.py) and register it with Claude Desktop using mcp install mcp_server.py. Key configuration options include --company, --pages, --since, and --enrich (requires ANTHROPIC_API_KEY).

Key features of LeetCode Crawler

Crawls LeetCode discussion forums by company tag (google, meta, amazon, etc.)
Classifies posts into types: leetcode, discuss_link, discussion
Deduplicates and groups discussions by title and company across pages
Optionally enriches posts with AI-extracted fields (problem name, stage, difficulty, outcome)
Saves results to CSV with per-company cache or monthly output
Exposes four MCP tools: search_discussions, get_hot_problems, get_thread, refresh

Use cases of LeetCode Crawler

Research LeetCode interview questions for a specific company (e.g., Google, Meta)
Analyze discussion frequency to identify hot problems for interview preparation
Enrich posts with extracted difficulty and interview stage for structured analysis
Cache data locally for offline or repeated use without re-crawling
Integrate with Claude Desktop or Claude Code for real-time question lookups

よくある質問

What MCP tools does the server expose?

The server provides four tools: `search_discussions(company, days)` to filter cached posts, `get_hot_problems(company, limit)` to get top problems by frequency, `get_thread(post_id)` for real-time single-thread fetch, and `refresh(company, num_pages, days, enrich)` to crawl and rebuild the cache.

What does AI enrichment cost?

Enrichment uses `claude-haiku-4-5-20251001` at approximately $0.001 per post. Already-enriched posts are skipped on re-run.

How is data cached and stored?

Data is cached in `./cache/{company}_questions.csv` as per-company CSV files. The `refresh` tool must be run once to populate the cache before using `search_discussions` or `get_hot_problems`.

What are the runtime requirements?

Python 3.12+ is required. The `ANTHROPIC_API_KEY` environment variable is needed only when using the `--enrich` flag or `enrich=True` in the MCP `refresh` tool.

What transport and authentication does the server use?

The MCP server communicates over stdio transport. No external authentication is required; the only authentication needed is the Anthropic API key for optional AI enrichment.