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-valuation what's a fair valuation for quantum.ai? compare .com vs .ai multiples"
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 enterprise domain valuation, liquid value modeling, and market multiple analytics.
calculate_domain_valuation: Calculates deep financial valuation model for a domain name based on TLD tier, character length, commercial liquid floor, and enterprise upside.
compare_sales_multipliers: Returns market valuation multiples across top TLDs (.com, .ai, .io, .org, ccTLDs) based on historical secondary market liquidity.
Related MCP server: mcp-server-domain-appraisal
Add this snippet to your claude_desktop_config.json:
{
"mcpServers": {
"domain-valuation": {
"command": "npx",
"args": ["-y", "mcp-server-domain-valuation"]
}
}
}Under MCP Settings, add a new stdio transport:
Command: npx -y mcp-server-domain-valuation
npm install -g mcp-server-domain-valuationOr run directly via stdio:
npx mcp-server-domain-valuationMIT License. Developed for the global AI agent and domain ecosystem.
Calculates deep financial valuation model for a domain name based on TLD tier, character length, commercial liquid floor, and enterprise upside.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain name to evaluate (e.g. cloudcompute.com) |
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 discloses nothing about whether the valuation is deterministic or heuristic, what permissions or data sources are needed, or how the 'enterprise upside' factor is derived. 'Deep financial valuation model' implies sophistication but conveys no verifiable 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?
One dense sentence with the verb and the driving factors front-loaded; no filler and nothing to trim.
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?
There is no output schema, so the description should explain what the calculation returns (a dollar figure, a range, component scores), and it does not. Combined with absent annotations on a tool whose output drives financial decisions, the definition is materially incomplete.
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 coverage is 100% and the schema already documents the single 'domain' parameter with an example, so the baseline of 3 applies. The description explains what inputs feed the model (TLD tier, character length, liquid floor, enterprise upside) but those are derived properties of the domain, not additional parameter guidance.
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 (calculates) and resource (financial valuation model for a domain name), plus the four factors driving the model. It does not differentiate itself from the sibling compare_sales_multipliers, which is the only missing element for a 5.
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 indication of when to use this versus compare_sales_multipliers, no prerequisites, and no exclusions. The agent must infer that valuation precedes comparison from the tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns market valuation multiples across top TLDs (.com, .ai, .io, .org, ccTLDs) based on historical secondary market liquidity.
| Name | Required | Description | Default |
|---|---|---|---|
| tld | Yes | The top-level domain without dot (e.g. com, ai, io) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It does add genuine behavioral context by naming the data basis ('historical secondary market liquidity'), which tells the agent what kind of numbers these are, but it says nothing about freshness, coverage limits, or the shape of the returned multiples.
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, and the data basis is stated immediately. It is slightly under-specified rather than verbose, which is the only knock against it.
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 no output schema and no annotations, the description is the only source of behavioral detail, and it leaves the core confusion unresolved: whether the tool takes one TLD and returns multiples for it or returns a cross-TLD table. For a simple one-parameter lookup that is an adequate but incomplete definition.
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%, with the tld parameter documented including the no-dot format example, so the baseline is 3. The description adds no parameter meaning beyond that, and its 'across top TLDs' phrasing actually muddies how the single tld input is used.
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 states a specific output ('market valuation multiples') and enumerates the TLD scope (.com, .ai, .io, .org, ccTLDs), so the resource is identifiable. However, it never distinguishes itself from the sibling calculate_domain_valuation, and it creates tension with its own schema: it claims to return multiples 'across top TLDs' while the only parameter accepts a single TLD.
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 statement of when to use this tool versus calculate_domain_valuation, and no prerequisites or exclusions are given. The agent is left to infer the split from the tool names alone.
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.
v1.0.0calculate_domain_valuationcompare_sales_multipliersScored across 2 tools
The two tools have distinct purposes: one computes a valuation for a specific domain, the other returns market multiplier benchmarks by TLD. They complement rather than overlap, though both sit in the valuation space and an agent might occasionally conflate 'valuation model' with 'market multiples'.
Both tools follow a clean verb_noun pattern (calculate_domain_valuation, compare_sales_multipliers) with consistent snake_case. Naming is fully predictable.
Two tools is thin for a domain-valuation server; a valuation workflow typically needs inputs, benchmarks, and comparison operations. It's borderline rather than broken, but feels under-scoped.
The surface covers a single valuation calculation plus TLD multipliers, but lacks obvious operations like batch valuation, historical/comparable sales lookup, or TLD listing. Agents can work around it but will hit dead ends for richer analyses.
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