scrapedatshi-mcp 0.1.3

MCP server for the scrapedatshi RAG pipeline API — use scrapedatshi tools directly from Claude Desktop

scrapedatshi-mcp

MCP (Model Context Protocol) server for the scrapedatshi RAG pipeline API.

Use scrapedatshi's scraping, crawling, extraction, and vector DB sync tools directly from Claude Desktop — no code required.

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What you can do

Just talk to Claude naturally:

• "Scrape https://docs.example.com and give me the chunks"
• "Crawl https://example.com/products and extract the title and price from every page"
• "Sync https://docs.example.com to my Pinecone index using OpenAI embeddings"
• "What embedding providers does scrapedatshi support?"

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Tools exposed

Tool	What it does
scrape_url	Scrape & chunk a single URL into RAG-ready text segments
crawl_site	Crawl an entire site (sitemap or spider mode) and return all chunks
extract_data	Extract structured schema fields from a URL using your LLM
extract_crawl	Multi-page schema extraction via site crawl
sync_to_vectordb	Full pipeline: scrape → embed → inject into your vector DB
list_embedding_providers	Discover supported embedding providers + model notes
list_vector_db_providers	Discover supported vector DBs + required config fields

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Prerequisites

1. scrapedatshi account — Sign up at scrapedatshi.com
2. Add credits — Billing portal
3. Get your API key — starts with sds_...
4. Claude Desktop — Download here
5. Python 3.10+ — python.org

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Installation

Option A — Install from PyPI (recommended, works with uvx)

pip install scrapedatshi-mcp

Or use uv for isolated installs:

uv tool install scrapedatshi-mcp

Option B — Install from source (local development)

git clone https://github.com/scrapedatshi/scrapedatshi-mcp.git
cd scrapedatshi-mcp
pip install -e .

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Claude Desktop configuration

Open your Claude Desktop config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

If installed via PyPI / pip (using uvx)

{
  "mcpServers": {
    "scrapedatshi": {
      "command": "uvx",
      "args": ["scrapedatshi-mcp"],
      "env": {
        "SCRAPEDATSHI_API_KEY": "sds_your_key_here"
      }
    }
  }
}

If installed via pip (using python)

{
  "mcpServers": {
    "scrapedatshi": {
      "command": "python",
      "args": ["-m", "scrapedatshi_mcp.server"],
      "env": {
        "SCRAPEDATSHI_API_KEY": "sds_your_key_here"
      }
    }
  }
}

If cloned from source (absolute path)

{
  "mcpServers": {
    "scrapedatshi": {
      "command": "python",
      "args": ["/absolute/path/to/scrapedatshi-mcp/scrapedatshi_mcp/server.py"],
      "env": {
        "SCRAPEDATSHI_API_KEY": "sds_your_key_here"
      }
    }
  }
}

Restart Claude Desktop after saving the config.

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Secure key configuration (BYOK)

You bring your own LLM, embedding, and vector DB keys. The server resolves keys in this priority order:

1. Argument passed in the tool call — explicit override
2. Environment variable in the MCP config — preferred secure path (keys never appear in chat)
3. Clear error message if neither is found

Add your provider keys to the env block in claude_desktop_config.json:

{
  "mcpServers": {
    "scrapedatshi": {
      "command": "uvx",
      "args": ["scrapedatshi-mcp"],
      "env": {
        "SCRAPEDATSHI_API_KEY": "sds_your_key_here",

        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GEMINI_API_KEY": "AIza...",

        "COHERE_API_KEY": "...",
        "MISTRAL_API_KEY": "...",
        "VOYAGE_API_KEY": "...",

        "PINECONE_API_KEY": "pc-...",
        "QDRANT_API_KEY": "...",
        "WEAVIATE_API_KEY": "..."
      }
    }
  }
}

Once set, Claude will automatically use these keys without asking you to type them in chat.

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Supported environment variables

Variable	Used for
SCRAPEDATSHI_API_KEY	scrapedatshi API key (required)
OPENAI_API_KEY	OpenAI LLM + embedding
ANTHROPIC_API_KEY	Anthropic LLM (Claude)
GEMINI_API_KEY	Google Gemini LLM + embedding
COHERE_API_KEY	Cohere embedding
MISTRAL_API_KEY	Mistral embedding
VOYAGE_API_KEY	Voyage AI embedding
PINECONE_API_KEY	Pinecone vector DB
QDRANT_API_KEY	Qdrant vector DB (optional for local)
WEAVIATE_API_KEY	Weaviate vector DB (optional for local)

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Example conversations

Scrape a single page

You: Scrape https://docs.example.com/getting-started and show me the chunks.

Claude calls scrape_url and returns the chunked content with token counts and credit usage.

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Crawl a documentation site

You: Crawl https://docs.example.com — just the first 5 pages.

Claude calls crawl_site with max_pages=5 and returns all chunks from all pages.

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Extract structured data from a product page

You: Extract the product name, price, and whether it's in stock from https://example.com/products/widget-pro

Claude calls extract_data with a schema it constructs from your request, using your OpenAI key from the env config.

---

Extract data from an entire product catalogue

You: Crawl https://example.com/products and extract the title and price from every product page. Limit to 10 pages.

Claude calls extract_crawl with max_pages=10 and returns per-page extraction results.

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Sync a page to your vector DB

You: Sync https://docs.example.com to my Pinecone index. The index host is https://my-index-abc123.svc.pinecone.io. Use OpenAI text-embedding-3-small.

Claude calls sync_to_vectordb. If OPENAI_API_KEY and PINECONE_API_KEY are set in your env config, no keys need to be typed in chat.

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Discover what's supported

You: What embedding providers does scrapedatshi support?

Claude calls list_embedding_providers and returns a formatted list with model notes.

You: What fields do I need to configure for Qdrant?

Claude calls list_vector_db_providers and returns the required and optional fields for each provider.

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Supported providers

Embedding providers

Key	Provider
openai	OpenAI (text-embedding-3-small, text-embedding-3-large, ada-002)
cohere	Cohere (embed-english-v3.0, embed-multilingual-v3.0)
gemini	Google Gemini (text-embedding-004, gemini-embedding-001)
mistral	Mistral (mistral-embed)
voyage	Voyage AI (voyage-3, voyage-3-lite, voyage-code-3)
ollama	Ollama local (nomic-embed-text, mxbai-embed-large, etc.)

Vector databases

Key	Provider
pinecone	Pinecone
qdrant	Qdrant
chroma	ChromaDB (local)
supabase	Supabase (pgvector)
weaviate	Weaviate
mongodb	MongoDB Atlas
azure_cosmos	Azure Cosmos DB (NoSQL)
azure_cosmos_mongo	Azure Cosmos DB (MongoDB API)
lancedb	LanceDB (local)

LLM providers (for extraction + contextual retrieval)

Key	Provider
openai	OpenAI (gpt-4o-mini, gpt-4o, etc.)
anthropic	Anthropic (claude-3-haiku, claude-3-5-sonnet, etc.)
gemini	Google Gemini (gemini-1.5-flash, gemini-1.5-pro, etc.)

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Billing

• Credits are deducted from your scrapedatshi account after each successful API call
• Failed requests are not charged
• Every tool response includes credits_used and credits_remaining
• LLM, embedding, and vector DB costs are billed directly by your chosen providers — scrapedatshi only charges for scraping and orchestration
• Top up at scrapedatshi.com/portal/billing

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Safety limits

To prevent runaway credit usage and client timeouts:

• crawl_site: defaults to 10 pages, maximum 200
• extract_crawl: defaults to 5 pages, maximum 50 per call

Claude will always confirm page limits with you before calling multi-page tools.

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License

MIT — see LICENSE