Fetch webpage with full JavaScript rendering. Use for SPAs and dynamic content. Price: $0.006
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to fetch | |
| timeout | No | Request timeout in ms (default 15000) | |
| waitFor | No | CSS selector to wait for before capturing content (e.g. ".content") |
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?
With no annotations, the description carries the full burden. It discloses the price ($0.006) and full JS rendering, which are useful behavior traits. However, it does not explain side effects, return format, or whether execution is read-only—a gap for a rendering tool.
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?
Extremely concise: two short sentences and a price tag. Every word adds value, and the core purpose and usage are 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?
No output schema exists, so the description should clarify what is returned, but it does not. While the purpose and cost are clear, the lack of return-format or error-behavior context leaves a notable gap for a dynamic-rendering fetch tool.
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%, so the baseline is 3. The description adds no extra parameter-specific meaning, but the schema already documents all three parameters adequately.
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?
States clearly the action ('Fetch webpage') and a distinguishing feature ('full JavaScript rendering'), explicitly targeting SPAs and dynamic content. This sets it apart from sibling tools like fetch_webpage and fetch_resilient.
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?
Provides direct guidance to use for SPAs and dynamic content, giving a clear context of applicability. It does not explicitly mention alternatives or exclusions, but the instruction implies when this tool is appropriate.
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.