Scrape any URL and return cleaned text content. Powered by Playwright headless browser. Returns title, content, word count.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to scrape | |
| max_length | No | Max content length in chars (default: 10000) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions using a Playwright headless browser, which hints at JavaScript rendering, but fails to disclose key behavioral aspects such as output truncation based on `max_length`, potential failures for dynamic or restricted URLs, or performance implications. The return fields are listed, but critical limitations and error behavior are omitted.
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 exceptionally concise, consisting of two short sentences that front-load the main action and output. Every word adds value: the verb, resource, output type, technology, and return fields. No filler or redundancies are present.
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 no output schema, the description covers the basic function and output fields adequately. However, it omits important contextual details such as when to use this tool versus `screenshot_url`, the default and maximum content length behavior, and potential edge cases. It is incomplete for full contextual understanding but sufficient for a minimal viable description.
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?
The schema already provides 100% coverage for both parameters: `url` and `max_length` with descriptions and constraints. The tool description adds no additional parameter context beyond the schema, so it does not go beyond the baseline. It neither duplicates nor enriches the schema, resulting in a standard score of 3.
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 purpose: scraping a URL and returning cleaned text content with title, content, and word count. It uses a specific verb ('scrape') and resource ('URL'), making it distinct from tools like `analyze_sentiment` or `screenshot_url`. However, it does not explicitly differentiate from sibling `screenshot_url`, which also operates on URLs, so it falls short of a 5.
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 like `screenshot_url` or `search_web`. It says 'any URL' but does not mention scenarios where this tool is preferred, any prerequisites, or exclusions. No usage context is provided beyond a straightforward statement of function.
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.