Take a full-page screenshot of any URL. Returns base64-encoded PNG image.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to screenshot | |
| width | No | Viewport width in pixels (default: 1280) | |
| height | No | Viewport height in pixels (default: 720) | |
| full_page | No | Capture full page scroll height (default: true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the screenshot is full-page and returns a base64-encoded PNG, which are useful details. However, it does not mention potential limitations (e.g., unsupported URLs, rendering delays) or side effects beyond fetching, leaving some behavioral ambiguity.
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 a single, focused sentence that immediately states the action and output format. It is front-loaded with the key information and contains no filler or redundant phrasing.
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?
Given the tool's moderate complexity (4 parameters, no output schema) and minimal annotations, the description adequately covers the core function and return value. It does not explain error handling or edge cases, but the essential behavior is clearly conveyed, leaving only minor gaps.
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 input schema provides 100% coverage with descriptions for all four parameters (url, width, height, full_page), including defaults and ranges. The description adds no additional parameter semantics, so a baseline of 3 is appropriate given the schema's completeness.
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 a specific action ('Take a full-page screenshot') and a resource ('any URL'), which uniquely distinguishes it from sibling tools like scrape_url that extract data. It also mentions the output format (base64-encoded PNG), making the purpose unambiguous.
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 the tool is for capturing visual representations of web pages, which is distinct from text extraction performed by scrape_url. However, it lacks explicit guidance on when to use this tool versus alternatives or any preconditions (e.g., page must be publicly accessible), so usage context is only implied.
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.