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.

What you can do

Just talk to Claude naturally:

• "Scrape https://docs.example.com and give me the chunks"
• "Chunk this PDF URL: https://my-bucket.s3.amazonaws.com/report.pdf" — PDF URLs are automatically detected and extracted
• "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"
• "Crawl the entire docs.stripe.com site (all 800 pages) and inject it into my Pinecone index" — large sites are auto-batched server-side, no manual pagination needed
• "What embedding providers does scrapedatshi support?"
• "Inspect my Pinecone index and tell me what embedding model was used"
• "Query my Pinecone index for information about API authentication"

Tools exposed

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

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 .

Claude Desktop configuration

Easiest way to find your config file: Open Claude Desktop → Settings → Developer → Edit Config

Alternatively, the file is located at:

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

Recommended — uvx with all provider SDKs (auto-updates on restart)

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

• [all] installs all provider SDKs (OpenAI, Anthropic, Gemini, Voyage AI) so verify_provider_key works for any provider
• --refresh checks PyPI for updates every time Claude Desktop starts — no manual reinstalls needed

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.

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": [
        "--from", "scrapedatshi-mcp[all]",
        "--refresh",
        "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.

Fetch Mode

Starting in v0.5.0, the MCP server uses local-fetch mode by default — URLs are fetched on the machine running Claude Desktop (your IP), and only the HTML processing runs on our server. This is cheaper and keeps your IP off our server.

SCRAPEDATSHI_FETCH_MODE=local (default)

The MCP server fetches URLs using the machine's own IP address, then submits the raw HTML to our server for processing.

• ✅ Your IP is used — not our server's
• ✅ Billed at the standard per-URL rate ($0.0020)
• ✅ Faster — no double-hop latency

SCRAPEDATSHI_FETCH_MODE=server

Our server fetches the URL. Use this if Claude Desktop is running in a restricted environment without outbound HTTP access, or if you need server-managed IP rotation.

• ⚠️ Our server's IP is used
• ⚠️ Billed at 2× the standard rate ($0.0040 / URL)
• ✅ Works from restricted environments

To enable server fetch, add SCRAPEDATSHI_FETCH_MODE to your MCP config:

{
  "mcpServers": {
    "scrapedatshi": {
      "command": "uvx",
      "args": ["--from", "scrapedatshi-mcp[all]", "--refresh", "scrapedatshi-mcp"],
      "env": {
        "SCRAPEDATSHI_API_KEY": "sds_your_key_here",
        "SCRAPEDATSHI_FETCH_MODE": "server"
      }
    }
  }
}

Supported environment variables

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.

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.

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.

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.

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.

Supported providers

Embedding providers

Vector databases

LLM providers (for extraction + contextual retrieval)

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

Per-URL rates

Auto-Batching for Large Sites

When you ask Claude to crawl a large site (more than 200 pages), the autorag and crawl_site tools automatically split the job into sequential batches server-side. You don't need to do anything special — just ask Claude to crawl the site and it handles the rest.

You: Crawl the entire docs.stripe.com site and inject everything into my Pinecone index.

Claude calls autorag with a high max_pages value. If the site has 600 pages, the server processes it as 3 batches of 200 pages each and returns the combined result.

The response will include auto_batched: true and batches_processed: N when batching occurred.

Safety limits

To prevent runaway credit usage and client timeouts:

• crawl_site: defaults to 10 pages, maximum 200 per batch (auto-batched for larger jobs)
• autorag: defaults to 5 pages, no hard upper limit — large jobs are auto-batched
• extract_crawl: defaults to 5 pages, maximum 50 per call

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

Troubleshooting

Contextual Retrieval fails — "model no longer available"

LLM providers periodically deprecate older models. If you see an error like "This model is no longer available", run verify_provider_key again to get the current list of available models for your key, then select a current model.

Current recommended models for contextual retrieval:

• Gemini: gemini-2.5-flash or gemini-2.0-flash-001 (not gemini-2.0-flash — deprecated)
• OpenAI: any current gpt-4o or gpt-4.1 series model
• Anthropic: any current claude-3-5 or claude-3-7 series model

Provider model & deprecation pages:

• OpenAI: platform.openai.com/docs/deprecations
• Anthropic: docs.anthropic.com/en/docs/about-claude/models
• Google Gemini: ai.google.dev/gemini-api/docs/models
• Cohere: docs.cohere.com/docs/models
• Mistral: docs.mistral.ai/getting-started/models
• Voyage AI: docs.voyageai.com/docs/embeddings

Contextual Retrieval fails — "quota exceeded"

Your LLM provider API key has no remaining credits. Add credits at your provider's billing page. Note that scrapedatshi credits are separate from your LLM provider credits — you need both.

verify_provider_key returns no models

If key verification succeeds but returns an empty model list, your API key may be restricted to specific model families or your account may have limited access. Check your provider's dashboard for account restrictions.

Claude Desktop doesn't show scrapedatshi tools

1. Make sure you saved claude_desktop_config.json correctly (valid JSON, no trailing commas)
2. Fully quit and reopen Claude Desktop — a simple window close is not enough
3. Check that uvx is installed: run uvx --version in your terminal
4. If using --refresh, the first startup may take a few seconds to download the package

License

MIT — see LICENSE