scrapedatshi-mcp 0.1.8

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
verify_provider_key	Verify an LLM or embedding API key + get live model list
get_usage_guide	Returns the guided wizard flow and tool selection reference
scrape_url	Scrape & chunk a single URL into RAG-ready text segments
chunk_file	Upload a local file (PDF, MD, TXT, etc.) and chunk it into RAG-ready 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 URL → embed → inject into your vector DB
ingest_file	Full pipeline: upload local file → embed → inject into your vector DB
autorag	Full pipeline: crawl entire site → chunk → 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

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.

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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": [
        "--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.

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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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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

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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.

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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.

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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

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License

MIT — see LICENSE