robotsParses robots.txt for any domain — shows crawl rules, disallowed paths, sitemaps. Agents use this to check if they're allowed to scrape a site before doing it. [free]
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Target webpage URL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It clearly indicates a read-only parse operation and enumerates the output categories, which is sufficient for a simple fetch-and-parse tool; error handling and network behavior are not discussed but are minor gaps.
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 compact sentences front-load the core action and include output details and a practical use case. The '[free]' marker is minor but not distracting.
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 one-parameter tool with no output schema, the description adequately covers what it does, what it returns, and when to use it. It could include more detail about input format edge cases, but the current description is complete enough for correct 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 covers the single url parameter at 100% with 'Target webpage URL'. The description adds that the tool resolves a domain's robots.txt from the URL, but it does not clarify whether a bare domain or full page URL is expected, so the added value is marginal.
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?
Describes a specific action and resource: 'Parses robots.txt for any domain' and lists concrete outputs (crawl rules, disallowed paths, sitemaps). This makes it easy to distinguish from sibling tools like scrape or page_meta.
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 clear when-to-use guidance: agents should check robots.txt permission before scraping a site. It does not name alternatives or explicit when-not-to-use cases, so it stops short of a full routing guideline.
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.