Scrape a URL then use AI to extract structured JSON data matching your schema description. Combines Playwright scraping with Grok LLM extraction.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to scrape | |
| schema_description | Yes | Description of the data to extract and desired JSON structure. Example: 'Extract all product names and prices as {products: [{name, price}]}' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that it uses Playwright scraping and Grok LLM extraction, which are relevant behavioral traits. However, it doesn't mention potential non-determinism of LLM output, failure modes, rate limits, or whether the action is read-only, leaving notable gaps.
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?
The description is two sentences, front-loads the primary action, and includes no filler. Every word contributes to understanding what the tool does and how it works.
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?
For a simple two-parameter tool with 100% schema coverage, the description is mostly complete. It explains the workflow and expected output (structured JSON), though it lacks explicit mention of return format details or error scenarios. Still, it provides sufficient context for effective use.
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 coverage is 100%, and both parameters have descriptions. The description adds minor value by clarifying that the schema_description defines the desired JSON structure, but it largely echoes what the schema already provides. The baseline of 3 is appropriate.
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 the tool's function: scrape a URL and use AI to extract structured JSON matching a schema description. It uses a specific verb and resource, and distinguishes it from siblings like scrape_url and screenshot_url by combining scraping with structured extraction.
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 implies when to use this tool: when you need structured JSON data from a webpage. It does not explicitly name alternatives or exclusions, but the context of combining scraping with AI extraction is clear enough for an agent to choose it appropriately.
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 has a clearly distinct purpose: sentiment analysis, structured data extraction, content generation, health check, tool listing, code review, web scraping, screenshot, and web search. There is minor overlap between scrape_url and extract_structured_data, but the different outputs (raw text vs. structured JSON) make them easily distinguishable.
Most tool names follow the verb_noun snake_case pattern (e.g., analyze_sentiment, scrape_url). The only outlier is 'health', which is a single noun rather than a verb_noun like 'check_health'. This minor inconsistency slightly reduces coherence.
With 9 tools, the server is well-scoped for a general-purpose utility toolkit. Each tool serves a useful function without redundancy, and the count is within the ideal range (3-15) for clarity and manageability.
The tool set covers a broad range of common AI/automation tasks such as text analysis, web scraping, content generation, search, and code review. While some potential utilities (e.g., translation or file conversion) are missing, the lack of a specific domain makes the set feel reasonably complete for a general-purpose toolkit.