v1_rdapRDAP whois: Structured domain/IP registration data (modern whois). Source: RDAP (rdap.org). $0.005 per call · GET /v1/rdap
| Name | Required | Description | Default |
|---|---|---|---|
| ip | No | IP address (alternative). Example: '8.8.8.8'. | |
| domain | No | Domain name. Example: 'example.com'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (read-only, idempotent, non-destructive), so the bar is lower. The description adds useful operational context such as the source (rdap.org), per-call cost, and HTTP endpoint, but it does not disclose response format, error behavior, or request limitations beyond the annotations.
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?
A single sentence front-loads the core purpose and packs in source, pricing, and endpoint without any wasted words. It is highly efficient and well-structured.
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?
The tool is simple, but with no output schema, the description could better explain what registration fields are returned and how to choose between the domain and IP parameters. The current text gives only a general sense of the output and leaves some operational detail to inference.
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 provides 100% coverage of both parameters, each with descriptions and examples, so the schema carries the burden. The description adds no additional parameter-level meaning beyond what is already in the schema.
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 identifies the tool as providing RDAP whois data for domains or IPs, with 'Structured domain/IP registration data (modern whois)' conveying the core function. It distinguishes the tool from generic DNS or geolocation lookups, though it does not explicitly name sibling tools.
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?
No guidance is given on when to use this tool versus alternatives like v1_dns or v1_ip_geolocation. The description only provides the data source and pricing, leaving the selection context implicit.
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.
Every tool targets a distinct resource or operation—geocoding, weather, DNS, VIN, stock quotes, etc.—with no meaningful overlap in purpose. Even the location- and finance-related tools are clearly separated by their descriptions.
All tools follow the same v1_<resource>[_modifier] snake_case pattern, such as v1_air_quality, v1_reverse_geocode, and v1_validate_email. Although the names are not verb-based, the convention is perfectly consistent across all 31 tools.
31 tools exceeds the 25+ threshold and creates a heavy selection burden for agents, even though the server's stated purpose is broad. Many endpoints are small single-purpose lookups that could be grouped into fewer combined tools without losing clarity.
As a general public-data utility, the set covers a wide range of common lookup categories: location, weather, finance, legal, health, business, internet, and reference data. It has minor gaps like historical financial time series or phone-number validation, but no obvious dead ends since all tools are self-contained read-only lookups.