NameWhisper MCP Server

get_valuation

Estimate an ENS name's value with confidence-rated pricing from comparable sales, entity recognition, search interest, word frequency, and fame. Get value range, context, comps, and methodology.

Instructions

Get a confidence-rated valuation for an ENS name based on comparable sales, entity recognition (Wikipedia/Wikidata), search interest, word frequency, and fame-scaled pricing. Returns estimated value range, background context on the name (person/place/brand/concept), comparable sales data, and a narrative explaining the valuation methodology. Essential for pricing decisions.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>"
    },
    "name": "get_valuation"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes	ENS name or label to value (e.g. "coffee" or "coffee.eth")

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Jul 31, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses what the tool returns (value range, background, comparables, narrative) and implies read-only behavior through 'Get'. It does not mention edge cases, data availability, or any side effects, but for a query tool this is reasonably transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose, and efficiently lists the return components. It is somewhat dense but every clause adds value, so it earns a high but not perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and the absence of an output schema, the description thoroughly enumerates all return values (range, background, comparables, narrative) and explains the valuation basis. It also states the primary use case, making it complete for an agent to understand and invoke.

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?

The schema already provides a complete description of the single 'name' parameter with 100% coverage, including an example. The description adds no additional semantic information about the parameter, so the baseline score of 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 clearly states the tool gets a confidence-rated valuation for an ENS name, with specific inputs (comparable sales, entity recognition, search interest) and outputs (value range, background, comparables, narrative). This distinguishes it from sibling tools like get_similar_names or check_availability.

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 phrase 'Essential for pricing decisions' provides clear context for when to use the tool. However, it does not explicitly name alternative tools or state when not to use it, so it lacks explicit exclusionary guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.