AI-powered structured data extraction from any webpage using natural language. Returns JSON matching your prompt or schema.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to extract from | |
| prompt | Yes | Natural language description of what to extract | |
| schema | No | Optional JSON schema for the response | |
| wait_ms | No | ||
| wait_for | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Minimal annotations (only title), description does not disclose authentication needs, error handling, or side effects. Missing behavioral traits beyond 'AI-powered' and 'returns JSON'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, 28 words, front-loaded with key information, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks details on return format beyond JSON, error handling, and purpose of wait_ms/wait_for parameters despite moderate complexity (5 params, no output schema).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
60% schema coverage provides baseline; description adds that output matches prompt/schema but does not explain wait_ms or wait_for parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly describes extraction of structured data from webpages using natural language, distinguishing it from siblings like scrape_webpage (raw HTML) and extract_metadata (metadata).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for structured data extraction but lacks explicit guidance on when to use vs. alternatives, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: session management, metadata extraction, AI-powered data extraction, multi-step workflows, content scraping, and screenshot capture. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern with underscores (e.g., create_browser_session, extract_metadata, run_workflow), making it predictable and easy to understand.
With 6 tools, the server covers the essential operations for web scraping and automation: session management, content retrieval, metadata extraction, structured data extraction, screenshots, and complex workflows. The count is well-scoped and not excessive.
The tool set covers core scraping tasks comprehensively, including advanced workflow automation. Minor gaps exist, such as no explicit tool for direct cookie manipulation or DOM inspection, but the workflow tool can compensate. Overall, it is sufficiently complete for typical use cases.