Model Context Protocol (MCP) server for authoritative raw WHOIS lookups, domain age calculation, and registrar detection.
raw_whois_lookup: Queries authoritative registries directly via socket port 43 to obtain live WHOIS records without API limits.
analyze_registration_lifecycle: Parses domain creation date and expiry date to evaluate domain maturity and renewal status.
Related MCP server: Whois MCP
Add this snippet to your claude_desktop_config.json:
{
"mcpServers": {
"whois": {
"command": "npx",
"args": ["-y", "mcp-server-whois"]
}
}
}Under MCP Settings, add a new stdio transport:
Command: npx -y mcp-server-whois
npm install -g mcp-server-whoisOr run directly via stdio:
npx mcp-server-whoisMIT License. Developed for the global AI agent and domain ecosystem.
Parses domain creation date and expiry date to evaluate domain maturity and renewal status.
| Name | Required | Description | Default |
|---|---|---|---|
| expiry_year | Yes | Domain expiry year | |
| creation_year | Yes | Domain initial creation year |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool parses and evaluates, implying a read-only computation, but it does not disclose what 'maturity' and 'renewal status' mean, whether the current date is used, or what exact output the agent should expect. This is a significant gap for a tool with no output schema.
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 with no filler. It efficiently conveys the tool's purpose and leaves the reader with the key behavioral orientation needed for selection.
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 two-parameter tool, this is minimally viable: the parameters are well-documented and the purpose is stated. However, with no output schema and no annotations, the description is incomplete about return values, status semantics, and how it differs from raw_whois_lookup in practice.
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%, so the input schema already fully explains creation_year and expiry_year. The description adds some relational meaning by linking these fields to maturity and renewal status, but it does not provide additional parameter-level guidance beyond what the schema already states.
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 uses a specific verb ('Parses') with clear inputs (creation date and expiry date) and explains the output purpose (evaluating domain maturity and renewal status). This clearly differentiates it from the sibling raw_whois_lookup, which focuses on raw data retrieval rather than interpretation.
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 gives a clear context for using the tool—analyzing registration lifecycle—but provides no explicit when-to-use guidance or exclusions relative to raw_whois_lookup. An agent must infer which tool to choose based on the presence of 'raw' in the sibling name rather than from direct routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Queries authoritative registries directly via socket port 43 to obtain live WHOIS records without API limits.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain name to lookup (e.g. google.com) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It does add useful context: direct socket port 43, authoritative registries, and no API limits. But it does not disclose output format, potential failure modes (e.g., blocked port 43 or per-registry restrictions), or whether results are raw text versus structured data.
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?
One single sentence with no redundant phrasing, no filler, and the key behavioral facts are front-loaded. Every word contributes to defining the tool's behavior and value.
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: one required parameter with full schema documentation. The description adequately explains the query mechanism and data source. Minor gap is the lack of detail about return data shape, but for a raw WHOIS lookup with no output schema the description is still sufficiently complete for an agent to invoke it correctly.
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% and the single required parameter already has a clear type, description, and example. The description adds no extra parameter semantics, but the baseline of 3 applies because the schema already does the heavy lifting.
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 uses a specific verb ('Queries'), a concrete resource ('authoritative registries'), a precise mechanism ('via socket port 43'), and a clear outcome ('live WHOIS records'). It also says 'without API limits', which differentiates it from API-based options and makes the purpose distinct from the sibling tool analyze_registration_lifecycle.
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 conveys clear context for when the tool is intended: obtaining live WHOIS records directly from authoritative registries with no API limits. However, it does not explicitly mention the sibling tool as an alternative, nor does it state exclusions such as when an RDAP-based or analysis-oriented tool should be preferred instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0analyze_registration_lifecycleraw_whois_lookupScored across 2 tools
The two tools have clearly distinct purposes: one fetches raw WHOIS data, the other analyzes registration lifecycle details from that data. There is no practical overlap or ambiguity in when to use each.
Both names use snake_case, but the pattern is inconsistent: 'raw_whois_lookup' is a noun phrase while 'analyze_registration_lifecycle' follows a verb-noun structure. The names are still readable and descriptive, but they do not share a uniform convention.
Two tools is on the thin end of the spectrum for a WHOIS-focused server. The pair covers raw lookup and one analysis function, which is borderline but arguably sufficient for a narrow use case.
The server provides raw WHOIS retrieval and expiry/creation analysis, but lacks common operations such as structured/parsed WHOIS output, registrar information, nameserver details, or domain availability checks. Agents needing standard WHOIS data beyond registration lifecycle would have to do their own parsing and may hit dead ends.
Domain intel for AI agents: RDAP registration, DNS, email deliverability, tech stack.
Domain intel for AI agents: RDAP registration, DNS, email deliverability, tech stack.
Domain search, registration, DNS, marketplace, and checkout with your AI agent.
Domain intelligence for DNS, WHOIS/RDAP, TLS, reputation, valuation, and brand protection.
