Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-server-domain-tools run a full domain audit on example.com"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Model Context Protocol (MCP) server providing an all-in-one Swiss Army knife suite for domain investors, webmasters, and autonomous agents.
full_domain_audit: Performs comprehensive domain audit including brand score, TLD tier analysis, and DNS connectivity checks.
generate_brand_variations: Generates strategic brand suffixes and industry compound variations for portfolio expansion.
Related MCP server: Domain Checker MCP Server
Add this snippet to your claude_desktop_config.json:
{
"mcpServers": {
"domain-tools": {
"command": "npx",
"args": ["-y", "mcp-server-domain-tools"]
}
}
}Under MCP Settings, add a new stdio transport:
Command: npx -y mcp-server-domain-tools
npm install -g mcp-server-domain-toolsOr run directly via stdio:
npx mcp-server-domain-toolsMIT License. Developed for the global AI agent and domain ecosystem.
Performs comprehensive domain audit including brand score, TLD tier analysis, and DNS connectivity checks.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain to audit |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full behavioral burden. It discloses the audit's component checks, which is useful, but says nothing about whether it is read-only, that it performs live network/DNS queries, expected latency, failure modes, or cost. For a tool that reaches external DNS infrastructure this is a notable gap.
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?
A single front-loaded sentence with no filler; the main verb and resource come first and the enumerated contents follow. Nothing is wasted, though it is a minimum-length statement rather than a carefully structured one.
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?
With only one parameter and no output schema, the description usefully names the three result categories, partially compensating for the missing return-value documentation. However, it omits the safety profile and any operational context an agent would need before invoking a network-touching audit.
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% for the single 'domain' parameter, so the schema already documents it fully. The description adds no format constraints (e.g., whether a subdomain or full URL is acceptable), so the baseline 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?
States a specific verb ('performs comprehensive domain audit') plus the resource ('domain') and enumerates the audit's components (brand score, TLD tier analysis, DNS connectivity checks). The sibling 'generate_brand_variations' is clearly a different verb, but the description never acknowledges or contrasts with it.
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 run this audit versus the sibling tool, no prerequisites, and no indication of whether results are cached or repeatable. The agent must infer usage entirely from the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generates strategic brand suffixes and industry compound variations for portfolio expansion.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | Base seed keyword (e.g. compute, cloud, pay) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden, yet it says nothing about how many variations are returned, whether results are deterministic, whether availability is checked, or what form the output takes. "Strategic" and "compound variations" convey tone but no operational behavior.
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?
A single front-loaded sentence with no filler or redundancy. It is efficient, though the brevity partly stems from under-specification rather than disciplined editing.
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 generator with no output schema and no annotations, the description should at least indicate the shape or quantity of generated output. It omits return format, count, and any indication of determinism, leaving the agent unable to predict results.
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?
There is only one parameter and schema description coverage is 100%, so the schema already documents "keyword" with examples (compute, cloud, pay). The description adds no syntax, constraints, or cardinality guidance beyond what the schema provides, making baseline 3 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?
States a specific verb ("Generates") and resource ("brand suffixes and industry compound variations"), so an agent knows the general operation. However, "strategic brand suffixes" is vague marketing language, and nothing distinguishes this from the sibling full_domain_audit.
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 only contextual hint is the trailing phrase "for portfolio expansion," which implies branding/domain work but gives no explicit when-to-use criteria, prerequisites, or alternatives. Sibling full_domain_audit is never referenced or contrasted.
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.
v1.0.0full_domain_auditgenerate_brand_variationsScored across 2 tools
The two tools have fairly distinct purposes: one performs an audit/analysis of a domain, the other generates new brand name variations. There is slight conceptual overlap since both operate on domain/brand data, but an agent can tell them apart.
Both names use consistent snake_case and are readable. However, 'full_domain_audit' is a noun phrase while 'generate_brand_variations' follows a verb_noun pattern, so the convention isn't perfectly uniform.
Only 2 tools is thin for a domain-tools server. The pairing (audit + generate) is coherent, but the surface feels minimal for a domain-focused toolkit.
Key domain operations are absent: no availability/whois check, no TLD listing, no DNS-only lookup, and no way to inspect or manage results. The audit covers scoring and connectivity, but agents would hit dead ends for common domain tasks.
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 intelligence for DNS, WHOIS/RDAP, TLS, reputation, valuation, and brand protection.
Screen expired domains: availability, drop stage, history, brandability, trademark risk.
