Scrape any webpage and return content as markdown, html, text, or json. Pay-per-call web scraping for AI agents.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to scrape (http or https) | |
| format | No | Output format (default: markdown) | |
| wait_ms | No | Milliseconds to wait after page load (max 10000) | |
| viewport | No | Viewport size (default: desktop) | |
| wait_for | No | CSS selector to wait for before extracting |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only include a title, so the description bears full responsibility. It does not disclose behavioral traits like rate limits, authentication needs, error handling, or that the tool executes JavaScript. The pay-per-call note is a billing detail, not a behavioral trait.
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 front-loaded with the core purpose. No redundancy. The cost note is secondary but concise. Efficient use of words.
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?
With 5 parameters and no output schema, the description lacks details on return format structure, size limits, error handling, or dynamic content behavior. It is minimal for a tool of this complexity.
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?
Schema description coverage is 100%, so baseline is 3. The description adds little beyond listing format options, which are already in the schema. No additional parameter meaning is provided.
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?
The description clearly states it scrapes any webpage and returns content in multiple formats (markdown, html, text, json). This differentiates it from sibling tools like screenshot_webpage (screenshots) or extract_metadata (metadata only).
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?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or compare with sibling tools. The 'Pay-per-call' note hints at cost but does not clarify usage context.
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.