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-evaluator evaluate brand quality and screen trademark risk for lumora.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 for multi-dimensional domain quality grading, radio test scoring, and trademark risk detection.
evaluate_brand_quality: Grades domain name on pronounceability, memorability, radio test, syllable balance, and visual symmetry.
screen_category_vs_trademark: Runs heuristic checks to distinguish generic category terms from high-risk corporate trademark violations.
Related MCP server: domain-mcp
Add this snippet to your claude_desktop_config.json:
{
"mcpServers": {
"domain-evaluator": {
"command": "npx",
"args": ["-y", "mcp-server-domain-evaluator"]
}
}
}Under MCP Settings, add a new stdio transport:
Command: npx -y mcp-server-domain-evaluator
npm install -g mcp-server-domain-evaluatorOr run directly via stdio:
npx mcp-server-domain-evaluatorMIT License. Developed for the global AI agent and domain ecosystem.
Grades domain name on pronounceability, memorability, radio test, syllable balance, and visual symmetry.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain name to evaluate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the evaluation dimensions an agent should expect, but says nothing about the return form (per-dimension scores, letter grade, rationale), determinism, or whether the input is treated as a bare name vs a URL — and there is no output schema to fill that 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?
One sentence, front-loaded with the verb and resource, and the criteria list is dense with no filler. Every clause earns its place.
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 simple one-parameter analysis tool this is close to adequate, but with no output schema the description should state what the 'grade' returns. Absent that, an agent knows what is evaluated but not what comes back.
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 a single required 'domain' parameter with 100% schema description coverage, so the schema already carries the semantics. The description adds no format details (e.g., whether to pass 'example.com' or 'example'), so 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 ('grades') and resource ('domain name'), then enumerates the exact criteria (pronounceability, memorability, radio test, syllable balance, visual symmetry). The purpose is unmistakable, but it makes no reference to its only sibling, screen_category_vs_trademark, so sibling differentiation relies on the reader noticing they address different concerns.
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?
There is no guidance on when to use this tool versus the trademark-screening sibling, nor any prerequisite or scope statement. Usage is only weakly implied by 'grades domain name'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Runs heuristic checks to distinguish generic category terms from high-risk corporate trademark violations.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | The term or SLD to screen (e.g. gulfcoastenergy vs subsearecovery) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. Saying 'heuristic checks' usefully signals approximate, non-authoritative results, which is genuine behavioral context. But it omits what the check returns, whether it is a binary risk flag or a scored judgment, permission requirements, and how 'high-risk' is determined.
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 sentence with no filler, and the core purpose is stated up front. Nothing could be trimmed without losing meaning.
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 screening tool with no annotations and no output schema, the description should explain what the heuristic produces and how its output should be interpreted. It also fails to position the tool against its only sibling, leaving the agent to guess at invocation context.
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 parameter is documented with clarifying examples (gulfcoastenergy vs subsearecovery). The description adds no further semantics beyond the schema, 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?
The description gives a clear verb and outcome: 'Runs heuristic checks to distinguish generic category terms from high-risk corporate trademark violations.' An agent understands the resource (a term/SLD) and the classification decision being made. However, it does not differentiate this tool from its sibling evaluate_brand_quality, leaving ambiguity about which to pick.
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?
There is no guidance on when to use this tool versus evaluate_brand_quality, nor any stated preconditions or exclusions. The name implies a screening context, but the description never says when this check is warranted over the sibling's brand-quality evaluation.
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.
v1.0.0evaluate_brand_qualityscreen_category_vs_trademarkScored across 2 tools
The two tools target clearly distinct evaluation dimensions: brand quality heuristics versus category/trademark risk screening. There is no apparent overlap in purpose, so an agent can easily select the correct tool.
Both tool names use a consistent snake_case, verb-first convention: evaluate_brand_quality and screen_category_vs_trademark. The pattern is predictable and readable.
Two tools is thin for a domain evaluator, as it likely needs additional checks such as availability, length, TLD suitability, or SEO considerations. The count is borderline rather than clearly appropriate.
The surface covers brand quality and trademark screening but omits other common domain evaluation operations like availability, extension checks, length scoring, or bulk evaluation. These gaps are notable for the stated server purpose.
Screen expired domains: availability, drop stage, history, brandability, trademark risk.
Trademark clearance (USPTO+TMview) and self-graded stock signals for AI agents. JSON verdicts.
AI-powered domain & business name generation with real-time availability checks.
AI-powered domain & business name generation with real-time availability checks.