whoisgenius
Server Details
Domain attribution and correlation for AI agents, with confidence and per-signal evidence.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.6/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: analyze_domain focuses on a single domain, while correlate_domains handles multiple domains. No overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern (analyze_domain, correlate_domains), making them predictable and easy to understand.
With only 2 tools, the set is minimal but appropriate for the focused domain analysis purpose. It covers the core operations without unnecessary bloat.
The tools cover single-domain analysis and multi-domain correlation, which are the main tasks. However, missing raw WHOIS data retrieval or other auxiliary functions leaves minor gaps.
Available Tools
2 toolsanalyze_domainBInspect
Analyze a domain name to identify its operator and attribution signals. Returns a human-readable Markdown summary and machine-readable structured output containing ranked entity candidates with confidence scores and per-signal evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Fully qualified domain name to analyze (e.g. 'example.com'). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It mentions the output format but does not clarify whether the operation is read-only, if authentication is required, or if there are rate limits or side effects. The description lacks critical transparency beyond what it returns.
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 extremely concise: two sentences that front-load the purpose and then detail the output format. Every word adds value, with no redundant or vague phrasing.
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?
Given the tool's simplicity (1 parameter, no output schema, no annotations), the description covers the basics: what it does and what it returns. However, it could be more complete by explaining terms like 'entity candidates' and 'per-signal evidence', which would help the agent understand the structured output better. It is minimally adequate.
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% (the single parameter 'domain' is well-described). The description adds no further semantic detail about the parameter beyond the schema, stating simply that it is a fully qualified domain name. Baseline 3 is appropriate since the schema already covers parameter meaning.
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 states the tool's purpose: analyze a domain name to identify operator and attribution signals. It specifies the output format (Markdown summary and structured output), providing a specific verb-resource pair. The sibling 'correlate_domains' is for correlation, so this tool's singular analysis focus is distinct.
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 on when to use this tool versus the sibling 'correlate_domains' or when not to use it. The description does not mention prerequisites, alternatives, or typical use cases, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
correlate_domainsAInspect
Correlate 2-15 domain names to detect shared infrastructure, common operators, and clustering evidence. Returns a human-readable Markdown summary and machine-readable structured output containing correlation clusters, pairwise scores, and shared signals.
| Name | Required | Description | Default |
|---|---|---|---|
| domains | Yes | List of domain names to correlate (2-15). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description fully carries behavioral disclosure burden. It mentions output format (Markdown summary + structured data) but does not state whether the operation is read-only, destructive, or requires permissions.
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 sentences: first states purpose, second describes output. Front-loaded, no fluff, every sentence adds 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?
For a tool with one parameter and no output schema, description adequately covers purpose and output. Lacks details on methodology or interpretation of clusters, but sufficient for selection.
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%; the schema already states 'List of domain names to correlate (2-15).' Description adds no further parameter meaning beyond what schema provides, meeting baseline.
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 specific verb 'correlate' with resource 'domain names' and clearly states purpose: detect shared infrastructure, operators, clustering evidence. Sibling tool 'analyze_domain' suggests single-domain analysis, making this distinct.
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?
Implies usage for correlating multiple domains but provides no explicit guidance on when to use vs. alternatives, nor when not to use. No context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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