similar domains
similar_domainsFinds similar/competitor domains by name-similarity. Lead-gen agents use this for prospecting. [price: $0.001/call USDC via x402]
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name |
ThirdMade Shelf
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It discloses the tool's core behavior ('Finds similar/competitor domains by name-similarity') and adds pricing information, but does not mention output format, result limits, or failure behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded, with two sentences plus a pricing note. Every sentence adds useful information: core purpose, target use case, and cost.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter lookup tool, the description covers purpose, method, use case, and cost. It does not specify the exact response shape, but the tool's simple nature and complete schema coverage make the description largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the only parameter is described as 'Domain name' in the schema. The description does not add extra parameter details, but the schema already provides sufficient semantic clarity, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Finds') and a specific resource ('similar/competitor domains') with a clear method ('by name-similarity'). It clearly distinguishes this tool from domain_facts and other sibling tools by focusing on lead-gen/prospecting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly identifies the intended use case: 'Lead-gen agents use this for prospecting.' This gives clear context for when to invoke it, though it does not explicitly mention exclusions or alternative tools.
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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TDQS
B3.2/5.0
Disambiguation3/5
Most tools have distinct purposes, but several clusters overlap: domain_facts, page_meta, and scrape all return page title information, and search_verify, hallucination_check, and sweep all target claim validation. The descriptions usually clarify the use case, but the boundaries are not always obvious.
Naming Consistency3/5
All names use lowercase snake_case, so there is a baseline consistency, but the pattern is mixed: bare verbs like scrape, summarize, and sweep sit alongside noun+noun forms like domain_facts and noun+verb forms like entity_find. The names are readable but do not form a predictable verb_noun API convention.
Tool Count3/5
At 26 tools, this is heavy and above the typical well-scoped 3-15 range, though the server is explicitly positioned as a broad shelf of paid utilities. Many tools are small one-purpose endpoints, so the count feels more like a catalog than a focused suite, but it is not an extreme mismatch.
Completeness4/5
The shelf covers the major advertised areas: web page analysis, research verification, text guards and NLP, blockchain reads, and image generation. There are some gaps such as no web search and no transaction sending, but agents can typically work around them or pair this with another server.