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Name Whisper — ENS Intelligence Layer

by ai.namewhisper

get_name_details

Read-only

Get full details for a specific ENS name including owner, expiry, creation date, tags, active listings (with prices in ETH and USD), active offers, ENSIP-25 agent identity bindings, and ENSIP-26 agent context records. creationDate is always populated — for Vickrey-era names (isVickreyEra=true) it reflects the true first-creation date from the 2017-2019 legacy auction contract; for all other names it reflects the on-chain registration date. Always mention creationDate when answering questions about when a name was created or registered. Also returns the name's resolver address and isPublicResolver flag — use these to answer resolver questions instead of guessing; bulk_set_records works for any name where isPublicResolver is true. Returns wrapped (true/false), and when wrapped also wrappedOwner, fuses, and cannotUnwrap — use these to answer wrap/unwrap/fuse questions from data; never guess wrapped status. Note: this makes NO wash-trading / authenticity judgment on a name's sales — for "is this name's sale history wash trading / fake?" use wash_check.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesENS name or label (e.g. "vitalik" or "vitalik.eth")

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true. The description adds valuable context about special behavior (e.g., creationDate handling for Vickrey-era names, wrapped status details) and explicitly states it makes no wash-trading judgment, adding transparency beyond annotations.

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 front-loaded with purpose, then lists returned fields, and provides structured guidance per field. While lengthy, every sentence adds value for a complex tool, and there is no redundancy or extraneous content.

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 lack of output schema, the description comprehensively covers all returned fields and their nuances (e.g., creationDate, resolver, wrapped status). It also addresses edge cases (Vickrey-era) and cross-tool references, making it fully informative for an agent.

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% with a clear parameter description. The description adds no additional information about the 'name' parameter beyond what the schema provides, so it meets the baseline but does not exceed it.

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 'Get full details for a specific ENS name' and lists specific fields, distinguishing it from siblings like get_primary_name and wash_check by explicitly noting what it does not do (e.g., wash-trading judgment).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: when to mention creationDate, use resolver fields instead of guessing, and notes the tool does not assess wash trading, directing to wash_check for that purpose. Also connects to bulk_set_records for resolver actions.

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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions, but some pairs (e.g., search_ens_names vs enumerate_entities, batch_purchase vs sweep) could cause confusion due to overlapping functionality. The descriptions help differentiate, but the large number of tools increases ambiguity risk.

Naming Consistency5/5

All tool names use consistent snake_case with a verb_noun pattern (e.g., get_name_details, set_ens_records, batch_create_listings). Naming conventions are uniform and predictable, making it easy to infer tool purpose from the name.

Tool Count4/5

With 44 tools, the server is quite comprehensive, covering a wide range of ENS operations. While this exceeds the typical 3-15 tool count, the scope of the domain (marketplace, registration, agent identity, etc.) justifies the number, and tools are well-organized into logical groups.

Completeness5/5

The tool surface covers the full lifecycle of ENS names: registration, renewal, wrapping, transfers, marketplace actions (listings, offers, purchases), record management, subnames, agent identity, and market intelligence. No obvious gaps are present for an ENS intelligence platform.

Resources