link_graphExtracts all outbound links from a page, classified by domain. Shows where a page links to and which domains dominate. [price: $0.001/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?
With no annotations, the description carries the burden of behavior disclosure. It explains the core behavior well—extract outbound links and classify by domain—but does not disclose edge cases such as whether same-domain links are included, redirect handling, pagination, or response format. This is adequate but not richly transparent.
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 two tight sentences that front-load the primary action and output, followed by a price note. Every sentence earns its place; there is no repetition of the tool name or schema contents.
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 extraction tool with no output schema, the description gives enough conceptual detail about the returned data—outbound links, domain classification, dominant domains—to let an agent understand what it will get. It does not detail exact return formatting, but that is not strictly necessary for a simple tool of this kind.
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 coverage is 100% and the only parameter, url, is already described in the schema as 'Target webpage URL.' The description adds no additional parameter meaning or format guidance, so the baseline score of 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?
Description uses a specific verb ('Extracts') and names a concrete resource ('outbound links from a page') with a clear output structure ('classified by domain'). It clearly differentiates from sibling tools like scrape, page_meta, and similar_domains by focusing specifically on link-level domain analysis.
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 description implies when to use this tool: when you need to understand where a page links and which domains dominate. It provides clear context but does not explicitly name alternatives or state when not to use it, so it misses the higher bar of explicit exclusion guidance.
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.