Captures a website screenshot as PNG/JPEG with performance metrics. Returns base64 image, Lighthouse performance score, First Contentful Paint, Largest Contentful Paint, and Total Blocking Time.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to screenshot | |
| format | No | 'json' for base64+metrics, 'image' for raw image (default: json) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose the return type (base64) and all included metrics, which is beneficial. However, fundamental behavioral details—such as rate limits, authentication needs, URL accessibility expectations, and the exact difference in behavior when format='image'—remain undisclosed.
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 that front-load the action and follow with a concise list of return values. No wasted words; it is appropriately minimal and scannable.
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 tool with only two parameters and a read-only purpose, the description covers the essential context: what it does, what it returns, and the format. It lacks minor details like timeout behavior or max image size, but given the low complexity, these are not critical gaps. The absence of an output schema is mitigated by the explicit enumeration of return fields.
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%, with both parameters (url and format) already documented. The description adds no additional parameter semantics beyond the schema; it never explains the allowed values for format (beyond the schema's own text) or the default behavior. Baseline of 3 applies because the schema is self-sufficient.
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 'Captures a website screenshot as PNG/JPEG with performance metrics' and enumerates the exact return data (base64 image, Lighthouse score, FCP, LCP, TBT). This specific verb+resource pairing leaves no ambiguity about what the tool does, even without comparing to siblings.
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 usage (any task needing a webpage screenshot with performance metrics) but does not explicitly say when to use this tool over alternatives like web_scrape or site_audit, nor does it mention when not to use it. The guidance is implicit but not direct.
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.
Several tools have overlapping functionality, such as domain_infra, company_report, and due_diligence all covering DNS/WHOIS/SSL checks. Similarly, web_scrape and scrape_structured both extract website content, and verify_email overlaps with email_audit on DNS-based email checks. This creates ambiguity in tool selection, especially for agents looking for a specific type of analysis.
All tool names use lowercase with underscores, which provides a consistent style. However, the grammatical pattern varies: some are verb-object (verify_email, currency_convert), some are noun-noun (domain_infra, site_audit), and others are adjective-noun (arabic_sentiment, brand_scout). This is not chaotic, but it lacks a rigid verb_noun convention, making it slightly less predictable.
With 25 tools, this sits at the upper boundary of what is considered 'heavy' but is still usable. The server covers a wide range of domains (Arabic NLP, web scraping, domain/email analysis, finance, faith), so the count is justified to a degree, but agents may be overwhelmed by choice. It is borderline appropriate for such a broad utility server.
The tool surface covers many common operations (scraping, DNS checks, email verification, financial data), but there are notable gaps. For example, no generic translation tool exists, only Arabizi-to-Arabic, and there is no text generation or embedding. While the set is extensive, it is not fully comprehensive for the diverse domains it touches, leaving some obvious missing operations.