Capture a screenshot of a webpage. Returns base64 PNG image. Price: $0.008
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to screenshot | |
| width | No | Viewport width (default 1280) | |
| height | No | Viewport height (default 720) | |
| fullPage | No | Capture full page scroll |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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 discloses the return format (base64 PNG) and cost ($0.008), which is useful. However, it does not mention any limitations, prerequisites, or behavioral characteristics beyond the basic action. For a non-destructive screenshot tool, this is sufficient but not rich.
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 extremely concise, with three short sentences covering purpose, output format, and price. Every sentence earns its place, with no fluff or redundancy. It is optimally front-loaded.
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 tool with only one required parameter and no output schema, the description adequately covers the core purpose, return format, and cost. It could benefit from a note about read-only behavior or usage alternatives, but given the low complexity and solid schema, it is nearly complete.
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 provides 100% coverage with descriptions for all four parameters, so the baseline is 3. The description does not add any additional meaning about parameters, only stating the overall purpose. It neither improves nor detracts from the schema's clarity.
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 with a specific verb and resource: 'Capture a screenshot of a webpage.' It also specifies the return format (base64 PNG), distinguishing it from sibling tools like fetch_webpage or crawl_site which retrieve textual content. This is unambiguous and unique.
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?
Usage is implied by the description and name—use when a visual screenshot is needed—but there is no explicit guidance on when to use it versus alternatives like fetch_webpage or crawl_site. No exclusions or alternative tools are mentioned, so it leaves the agent to infer 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.
Several tool clusters have near-overlapping purposes: fetch_webpage/fetch_webpage_pro/fetch_resilient and batch_fetch/get_contents are hard to distinguish, and answer_question/research/deep_research differ mainly in price and depth. The search_* and intel_* families are clearer, but the core fetching and research overlap creates ambiguity.
Most tools follow a verb_noun snake_case pattern (fetch_webpage, search_web, extract_data), but there are notable exceptions like domain_intel, package_intel, youtube_transcript, memory_set, and intel_company, where the prefix/suffix convention is inconsistent. Still, the naming is broadly readable.
35 tools is a large surface, far beyond the typical 3-15 range. The server covers many research verticals, but the number feels bloated, especially with multiple fetch and research variants that could be consolidated.
The tool set covers a wide range of web research needs: searching, fetching, crawling, extracting, screenshots, domain/tech/package intelligence, and market/competitive analysis. It lacks obvious lifecycle operations for monitors (list/delete/update) and memory (get/delete), but core workflows are well covered.