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Name Whisper — ENS Intelligence Layer

by ai.namewhisper

batch_purchase

Destructive

Buy up to 20 SPECIFIC listed ENS names in a SINGLE Seaport transaction, across NameWhisper, OpenSea, AND Grails listings.

Use this when the user names the exact names to buy. To buy the cheapest N names in a category/collection (a floor sweep), use the 'sweep' tool instead.

It's one signature and amortizes gas, far cheaper than calling purchase_name once per name. For each name it picks the cheapest listing (NameWhisper, OpenSea, or Grails), loads its Seaport order, and packs them into one fulfillAvailableAdvancedOrders call. NFTs are delivered directly to the buyer's wallet. Seaport skips any order that sold/cancelled since discovery and refunds the excess — partial fills are safe. The response lists which names made the batch (with marketplace + price) and which were dropped.

Any listing that can't be batched (e.g. a rare restricted-zone Seaport order) is returned in 'failed' — buy those individually with purchase_name. For a single name, purchase_name is also fine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesYesArray of ENS names to buy (e.g. ["coffee.eth", "tea.eth"]). Max 20 per batch.
walletAddressYesBuyer wallet address — receives all purchased names.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations indicate destructive hint (true). Description adds significant context: picks cheapest listing, uses Seaport partial fills for safety, skips canceled/sold orders, and details response format. No contradiction with annotations.

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?

Description is comprehensive and well-structured, front-loading purpose and usage. Slightly verbose but all information is valuable.

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?

Covers tool purpose, usage context, safety (partial fills), failure handling, and provides enough detail about return format despite no output schema.

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%, so baseline is 3. Description reinforces parameter purpose but adds no new semantic details 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 clearly states the tool buys up to 20 specific ENS names in a single Seaport transaction across multiple marketplaces. It distinguishes from sibling tools like 'sweep' (floor sweep) and 'purchase_name' (single name).

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?

Explicitly says to use when the user names exact names, and provides alternatives: use 'sweep' for floor sweeps and 'purchase_name' for single names or failed items.

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