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Scrapezy

Scrapezy

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@scrapezy/mcp MCP Server

A Model Context Protocol server for Scrapezy that enables AI models to extract structured data from websites.

Features

Tools

  • extract_structured_data - Extract structured data from a website

    • Takes URL and prompt as required parameters

    • Returns structured data extracted from the website based on the prompt

    • The prompt should clearly describe what data to extract from the website

Related MCP server: spider-cloud-mcp

Installation

Installing via Smithery

To install Scrapezy MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @Scrapezy/mcp --client claude

Manual Installation

npm install -g @scrapezy/mcp

Usage

API Key Setup

There are two ways to provide your Scrapezy API key:

  1. Environment Variable:

    export SCRAPEZY_API_KEY=your_api_key
npx @scrapezy/mcp

  2. Command-line Argument:

    npx @scrapezy/mcp --api-key=your_api_key

To use with Claude Desktop, add the server config:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "scrapezy": {
      "command": "npx @scrapezy/mcp --api-key=your_api_key"
    }
  }
}

Example Usage in Claude

You can use this tool in Claude with prompts like:

Please extract product information from this page: https://example.com/product
Extract the product name, price, description, and available colors.

Claude will use the MCP server to extract the requested structured data from the website.

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

License

MIT

Available Tools

1 tool
extract-structured-dataC

Extract structured data from a website.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesPrompt to extract data from the website
urlYesURL of the website to extract data from

TDQS

C2.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool extracts data but doesn't describe how it behaves: e.g., whether it makes HTTP requests, handles authentication, respects robots.txt, has rate limits, or returns errors. This leaves the agent guessing about operational details, which is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy to parse quickly. Every word earns its place by conveying the core action and target, though it could benefit from more detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (data extraction from websites) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'structured data' means, how extraction works, what the return format might be, or any behavioral traits. This leaves the agent with insufficient information to use the tool effectively in real-world scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with clear descriptions for both parameters: 'url' as the website URL and 'prompt' as the extraction prompt. The description adds no additional semantic context beyond the schema, such as examples of valid prompts or URL formats. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Extract structured data from a website' clearly states the action (extract) and resource (structured data from a website), but it's somewhat vague about what 'structured data' entails. It doesn't specify the type of data (e.g., tables, product info, contact details) or extraction method, leaving room for interpretation. No sibling tools exist for comparison, so differentiation isn't applicable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or limitations. It lacks context such as whether it's for scraping, parsing HTML, or using APIs, and doesn't mention any constraints like website accessibility or rate limits. Without siblings, it doesn't need to distinguish from them, but still offers no usage instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedextract-structured-data
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TDQS

C2.9/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as extracting structured data from websites, leaving no room for confusion.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern ('extract-structured-data'), and since there are no other tools, consistency is inherently perfect with no deviations or mixed conventions.

Tool Count2/5

One tool is too few for a server named 'Scrapezy', which suggests a scraping or data extraction domain. A single tool limits functionality and feels thin, as typical scraping tasks might require multiple operations like listing URLs or handling different data formats.

Completeness2/5

The tool surface is severely incomplete for a scraping domain. It only provides extraction, with obvious gaps such as no tools for discovering URLs, navigating pages, handling authentication, or managing scraping sessions, which will likely cause agent failures in real-world scenarios.

Maintenance

ActivityInactive
ResponsivenessNo issues

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