scrapeScrapes any public web page and returns clean structured JSON (title, text, length). The workhorse — agents use this for real data extraction. [price: $0.01/call USDC via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Target webpage URL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses that pages must be public, that output is normalized JSON, and the cost per call. It omits important caveats such as JS rendering, rate limits, and failure modes, so an agent may over-trust the claim 'any public web page'.
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?
Two functional sentences plus a cost note, with the core purpose front-loaded. No wasted words; the 'workhorse' line earns its place by conveying usage priority.
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 single-parameter tool with no output schema, the description provides the essential call context: public-only scope, output fields, and price. It could be more complete with an example or failure-mode note, but nothing critical is missing for basic invocation.
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 documents the only parameter ('url') with 100% coverage. The discription adds no syntax or format detail about the URL, but none is urgently needed for a single self-evident parameter; the baseline 3 applies.
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 opening sentence names the specific verb ('scrapes'), the resource ('any public web page'), and the exact output shape ('title, text, length'), which distinguishes it from sibling tools like page_meta or link_graph. 'The workhorse' reinforces its general-purpose role.
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 phrase 'agents use this for real data extraction' gives clear usage context and implies it is the default tool for this job. It does not explicitly state when NOT to use it or name an alternative, but it provides more guidance than most tool descriptions.
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.
Most tools have distinct purposes, but several clusters overlap: domain_facts, page_meta, and scrape all return page title information, and search_verify, hallucination_check, and sweep all target claim validation. The descriptions usually clarify the use case, but the boundaries are not always obvious.
All names use lowercase snake_case, so there is a baseline consistency, but the pattern is mixed: bare verbs like scrape, summarize, and sweep sit alongside noun+noun forms like domain_facts and noun+verb forms like entity_find. The names are readable but do not form a predictable verb_noun API convention.
At 26 tools, this is heavy and above the typical well-scoped 3-15 range, though the server is explicitly positioned as a broad shelf of paid utilities. Many tools are small one-purpose endpoints, so the count feels more like a catalog than a focused suite, but it is not an extreme mismatch.
The shelf covers the major advertised areas: web page analysis, research verification, text guards and NLP, blockchain reads, and image generation. There are some gaps such as no web search and no transaction sending, but agents can typically work around them or pair this with another server.