Domain attribution and correlation for AI agents, with confidence and per-signal evidence.
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Average 4/5 across 2 of 2 tools scored.
The two tools are clearly distinct: analyze_domain handles a single domain's operator and attribution, while correlate_domains examines multiple domains for shared infrastructure and clustering. There is no overlap in their core functions or expected inputs.
Both tool names follow the verb_noun pattern (analyze_domain and correlate_domains). The slight pluralization difference is minor and does not break the consistent convention.
With only two tools, the server feels minimal. The scope is narrow and well-defined, but two tools is on the thin side, making it borderline for a domain intelligence service that could offer additional lookups or search functions.
The server covers the core needs of single-domain analysis and multi-domain correlation, which are the primary stated purposes. It lacks additional features like raw WHOIS lookup or domain search, but these are not essential to its stated attribution-focused mission.
analyze_domainAInspectAnalyze 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'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of explaining behavior. It clearly describes the return format (human-readable Markdown summary and machine-readable structured output) and the nature of the output (ranked entity candidates, confidence scores, per-signal evidence). It does not disclose side effects or limitations, but for an analysis tool this is helpful.
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 sentences long, front-loaded with the purpose, and contains no fluff. Every phrase adds value: the goal, the output format, and the output 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 tool with one parameter and no output schema, the description is largely complete: it states the purpose, the parameter, and what the return values contain. It lacks explicit usage exclusions or edge-case handling, but the information provided is sufficient for an agent to know what the tool does and what it will receive.
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 already provides 100% coverage for the only parameter ('domain' with a description). The tool description does not add further meaning beyond what the schema states, so the baseline 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?
The description uses a specific verb ('analyze') and resource ('domain name') with a clear goal ('identify its operator and attribution signals'). It distinguishes from the sibling 'correlate_domains' by focusing on single-domain analysis, though it does not explicitly contrast with that tool.
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 use when operator/attribution information is needed for a single domain, but it does not state when to avoid this tool or mention the sibling 'correlate_domains' as an alternative. Guidance is limited to the inferred purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
correlate_domainsAInspectCorrelate 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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return format (human-readable Markdown and machine-readable output) and the key components (clusters, pairwise scores, shared signals). This gives good insight into behavior, though it doesn't mention potential side effects or limitations.
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 concise sentences, front-loaded with the primary action. Every word earns its place, with no repetition of schema information or filler.
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 a simple single-parameter schema, no output schema, and no annotations, the description adequately covers the core behavior and output. It specifies the number of domains, the analysis purpose, and the output structure. A minor gap is lack of an explicit example or further detail on what 'shared signals' means, but it remains sufficiently complete.
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% because the only parameter 'domains' is fully described with min/max items and item type. The description adds no extra semantic meaning beyond the schema, so baseline 3 is appropriate.
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 ('Correlate') and clearly states the resource (2-15 domain names) and the analytical goal (detect shared infrastructure, operators, clustering evidence). This distinguishes it from the sibling tool analyze_domain, which focuses on a single domain.
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 clearly implies this tool is for batch correlation of multiple domains (minimum 2), which indirectly separates it from analyze_domain for single domains. However, it doesn't explicitly state when to prefer this over the sibling or mention any 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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