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'. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / domain / examplesAdded value: +[
+ "example.com"
+]Input schema / properties / ip / examplesAdded value: +[
+ "8.8.8.8"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful context beyond that: the data source (rdap.org), the HTTP method (GET), and per-call pricing, which is relevant for cost-aware agent decisions.
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 tight sentence: it front-loads purose, then includes source, price, and endpoint metadata. No word is wasted.
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 description is adequate for a low-complexity read-only lookup, but it does not explicitly state that a domain or IP value must be provided, and there is no output schema to document response shape. Since the tool is otherwise well-annotated, this is a moderate gap rather than a critical one.
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 description coverage is 100%, with clear descriptions and examples for both 'domain' and 'ip'. The description adds little beyond indicating that the tool handles domain/IP registration data, and it does not clarify that one of the two parameters should typically be supplied even though neither is marked required.
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 RDAP as the resource and states it returns structured domain/IP registration data, distinguishing it from related tools like v1_dns or v1_ip_geolocation. It lacks an explicit action verb like 'get' or 'retrieve', so it stops short of a 5.
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 'modern whois' and 'domain/IP registration data' implies the tool is for registration lookups rather than DNS resolution or geolocation. However, there is no explicit guidance on when to choose this tool over specific siblings, and no stated exclusions or alternatives.
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.
Each tool maps to a distinct public data source and lookup type, from weather and geocoding to legal codes and vehicle VINs. Even adjacent tools like weather vs. weather_alerts or geocode vs. reverse_geocode are clearly separated by resource and direction.
All tools share a consistent v1_ prefix and snake_case resource naming, making the pattern predictable. Minor inconsistencies exist: most names are noun phrases (v1_country, v1_stock_quote) while a few are verb-led (v1_geocode, v1_validate_email, v1_unit_convert), and some abbreviations like v1_cfr and v1_lei are less descriptive.
At 31 tools, the surface is large and will strain agent tool-selection, even though each tool is individually useful. The broad 'public data utility' scope explains the count, but the calibration threshold of 25+ tools indicates an oversized set for practical use.
For a lookup-oriented public data utility, the set covers a wide range of common needs—weather, finance, location, legal/medical codes, domain/network, and conversions—without dead ends. It lacks some obvious public data categories (e.g., web search, population/census, news) and enumeration endpoints, but agents can work around these gaps.