domain_factsLive-checks a domain: is it up, does it have TLS, what's the title? Quick domain health check for agents evaluating links. [price: $0.001/call USDC via x402]
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name |
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 transparently discloses the tool's behavior: it performs a live check and reports three specific facts (up status, TLS presence, title). It also adds the pricing detail. It does not specify mechanics like timeouts or error handling, but the core behavioral traits are clearly conveyed.
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 front-loaded sentence that states what the tool does, what it evaluates, and the intended audience, followed by a separate pricing note. There is no redundancy or filler; every part earns its place.
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?
With no output schema, the description explains the key return data points ('is it up, does it have TLS, what's the title?'), which is the core output an agent needs. It also includes cost and use-context. It doesn't describe return formatting or failure modes, but for a one-parameter, low-complexity tool this is essentially 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 input schema has 100% description coverage for the single 'domain' parameter, so the description need not add much. It mentions 'a domain' in the text, but adds no format, example, or edge-case guidance beyond what the schema already provides. Baseline 3 is appropriate.
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 ('Live-checks a domain') and the specific data points checked (up status, TLS, title). This is enough to distinguish it from sibling tools like similar_domains, page_meta, and scrape based on the unique health-check focus.
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 'Quick domain health check for agents evaluating links' gives clear context for when the tool should be used. It does not explicitly name alternatives or exclusions, but the intended use case is sufficiently scoped for an agent to select it appropriately.
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.