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Name Whisper — ENS Intelligence Layer

by ai.namewhisper

search_ens_names

Read-only

Search ENS names using natural language. Supports all query types:

  • Filtered search: "4-letter words under 0.1 ETH"

  • Concept search: "ocean themed names" (semantic similarity across 3.5M indexed ENS names)

  • Creative search: "names for a coffee brand" (AI-generated suggestions)

  • Collection search: "crypto terms expiring soon"

  • Activity: "what sold recently?"

  • Availability check: "is coffee.eth taken?"

  • Bulk check: "check apple.eth, banana.eth, cherry.eth"

  • Collection/club floor: "999 club floor", "cheapest 10k club names" (returns real listings sorted by price)

  • Sorted pattern list: "3-letter numbers sorted by name", "4-letter words by registration date" (sorts by name, price, length, expiry or registration date) Returns structured results with name, price, owner, tags, and availability info.

It searches the NAME database by pattern/length/price/club/vibe — it does NOT know who real-world people, teams, brands, athletes, musicians, or films are. For "find me NBA players / pop stars / Pixar films / presidents" use enumerate_entities instead (it returns correctly-spelled labels). Use this for "floor of " / "cheapest in " (find_alpha can't — it has no collection param). For lifecycle-window lists — "which names are in premium / Dutch auction", "names in grace period", "expiring soon" — use get_expiring_names instead: its grace/premium statuses are on-chain-validated and premium rows carry live pricing. That hand-off needs a lifecycle WORD in the query. A plain pattern/length/digit list — "3-letter numbers sorted by name", "4-letter words", "5-digit names" — stays HERE even though it asks for a list with filters and a sort order; routing it to get_expiring_names answers a question the user never asked (a grace/premium dump instead of the names).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language search query (e.g. "cheap 3-letter words", "ocean themed names", "is coffee.eth taken?")

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses behavioral limitations such as not understanding real-world identities and not having collection-floor support, while also clarifying that it can still answer club floor queries. It also reveals what the response contains — 'name, price, owner, tags, and availability info' — which meaningfully informs an agent about expected results.

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?

Although the description is long, the bulleted query-type list is compact and scannable, and the routing guidance is dense with decision-relevant information. Every section earns its place; there is no filler or repetition of schema 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?

For a free-form search tool with one parameter and no output schema, the description is unusually complete: it explains supported query categories, result contents, boundary conditions, and sibling-tool routing. An agent has enough context to select this tool correctly and to frame an effective query.

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

Parameters5/5

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

The schema defines only a single 'query' string, and the description expands its semantics considerably by enumerating natural-language query categories and concrete examples. Since schema coverage is 100%, the description is not required to document parameter basics, but it adds substantial behavioral nuance beyond the schema.

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 opens with a specific verb and resource — 'Search ENS names using natural language' — and then enumerates the full breadth of query types it supports. It also clearly distinguishes this tool from siblings by stating what it does NOT know (real-world people, teams, brands) and where to route those queries.

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?

The description provides explicit routing guidance: use enumerate_entities for real-world entity searches, find_alpha is unsuitable for collection floor queries, and get_expiring_names is the right choice for lifecycle-window queries. It even explains why plain pattern/length-list queries should stay with this tool, preventing common misrouting.

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