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Name Whisper — ENS Intelligence Layer

by ai.namewhisper

enumerate_entities

Read-only

Enumerate ENS-friendly labels for a finite real-world entity category and report which are available vs registered.

USE THIS for ANY finite set of real-world people, companies, teams, or works — including queries that name a ROLE or PROFESSION rather than a league, e.g. "which tech founders have an available .eth?", "available CEOs / politicians / authors / footballers", "famous musicians I can register", "NBA hall of famers", "available Pixar films", "F1 drivers", "Beatles songs that are open". If the user is asking to find/register the names of actual real-world entities (not a vibe or an ENS club), this is the tool — even when the category sounds soft ("tech founders", "crypto founders", "famous CEOs") it is still a finite real-world list, so come straight here; do NOT fall back to search_ens_names for it. The tool generates verified, correctly-spelled ENS labels — do NOT enumerate entity names from your own context and pass them to check_availability, because models routinely misspell long-tail names (scottiepippin instead of scottiepippen) or invent people who don't exist (e.g. "johncarlton" as an NBA HOFer). This tool exists precisely to avoid that.

DO NOT use this for:

  • Vibes / themes ("luxury watch names", "edgy crypto names") — use search_ens_names with concept_search instead.

  • ENS-native categories ("10k club", "3-letter words") — use search_ens_names with collection_search.

  • Single-name lookups — use check_availability.

Returns a list of entries grouped by status. Each entry has the proper name (e.g. "Scottie Pippen") alongside the ENS label (scottiepippen.eth), so you can show users the human-readable name in your reply.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoWhich slice to return. "available" = registerable names only (most common for "what can I buy" queries). "registered" = names already owned. "all" = both, with status. Default: all.all
categoryYesReal-world entity category ONLY — e.g. "NBA hall of fame players", "Pixar feature films", "F1 world champions", "US presidents", "Italian cities", "Beatles songs". Be specific ("NBA hall of famers" good; "basketball players" too broad), but pass a CLEAN category with NO extra qualifiers: strip availability/quality/price words such as "available", "cheap", "star", "famous", "best", "top" — those are not part of the category and pollute the lookup. For availability, set the "filter" param instead; never put it in the category text.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true. The description explains return format (list grouped by status, proper name + ENS label) and warns about model misspellings. No contradiction with annotations. Slight deduction for not explicitly stating idempotency or side effects, but read-only is adequately conveyed.

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?

Well-structured and front-loaded with core purpose. Each paragraph adds value. Could be slightly more concise, but the length is justified by the need to clarify usage boundaries and parameter nuances. No redundant sentences.

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 only 2 parameters and no output schema, the description provides all necessary information: purpose, usage boundaries, parameter details, return format with examples, and behavioral warnings. An agent can correctly decide when and how to use this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%. The description adds valuable context beyond the schema: for 'category', it provides specificity rules (e.g., strip qualifiers like 'available', 'famous') and corrects common mistakes. For 'filter', it explains default and typical usage. Adds meaning beyond enum values.

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 enumerates ENS labels for finite real-world entity categories and reports availability vs registration. It provides specific examples (NBA hall of famers, Pixar films) and distinguishes from sibling tools like search_ens_names.

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?

Extensive guidance on when to use (finite real-world categories including roles/professions) and when not (vibes/themes, ENS-native, single lookups). Explicitly names alternatives (search_ens_names, check_availability) and gives examples of proper category formulation.

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.

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