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Name Whisper — ENS Intelligence Layer

find_alpha

Read-only

Scan the ENS marketplace for alpha — names listed below their valuation. Returns ranked opportunities with a discount %, fair-value range, confidence rating, and comparable data. Candidates are selected by DESIRABILITY (real curated collections, short, accessibly priced above a floor that excludes 0.001-ETH floor-dumps), then each is precision-priced by the full Name Whisper valuation engine — the SAME engine behind get_valuation and the Value page — which is the sole judge of undervaluation. The returned fair-value range (estimatedValueEth), confidence and discountPct are the engine's own numbers, via the same cache-first path as get_valuation (with display-only signals disabled for speed), so they are authoritative and consistent with get_valuation. They are computed conservatively (the seller-wallet boost is off), so if anything they slightly UNDERSTATE fair value — report them as-is; do NOT inflate the fair value or upgrade the confidence. Use estimatedValueEth.mid as the fair-value anchor.

Only opportunities the engine confirms are surfaced: a believable discount band (20%+, capped where valuations stop being reliable), MEDIUM+ confidence, and a REAL comparable-sale match (type/collection/word/entity/semantic — never a coarse same-length average). This means genuinely good, believable deals (typically 25–65% off) — not 99%-off junk. It will still surface a large discount when the engine confirms it with real comps; it just won't fabricate one.

Use this instead of search_ens_names + repeated get_valuation when the user asks for "best value", "best buy", "cheapest good name", "undervalued", "bargains", or any ranked-by-value query across multiple listings. find_alpha does the search + engine valuation + ranking in a single call — you do NOT need to call get_valuation again on its results. If it returns fewer names than asked, the rest weren't genuine discounts vs the engine — say so rather than padding the list. Supports filters (minLength, maxLength, maxPriceEth, charType) so narrow queries like "4-letter names under 1 ETH, best value" are one call, not six.

It has NO collection/category/club param. Do NOT use it for "floor price of the 999 club", "cheapest 10k-club names", or "floor of " — those name a specific collection, so use search_ens_names (which returns that collection's real listings sorted by price), or sweep if the user wants to buy the cheapest N. find_alpha is for value-ranked discovery across the market, not a named collection's floor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return. Default 25, max 100.
charTypeNoFilter by character type
maxLengthNoMaximum label length (e.g. 5 for up to 5-letter names). Omit for no cap — never pass a huge sentinel number.
minLengthNoMinimum label length (e.g. 3 for 3-letter names and up)
maxPriceEthNoMaximum listing price in ETH (e.g. 1.0). Omit for no cap — never pass a huge sentinel number.
minConfidenceNoMinimum engine confidence. Floored at MEDIUM — find_alpha never returns LOW-confidence (thin-comp) picks. HIGH = 20+ comps, MEDIUM = 10+.MEDIUM
minDiscountPctNoMinimum discount vs the engine fair value. Default 20%. Range: 1-99.

TDQS

A4.9/5.0
Behavior5/5

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

Goes far beyond the readOnlyHint annotation, detailing the selection criteria (desirability, confidence, comps), valuation engine behavior (conservative, cache-first), and constraints (discount band, min confidence). Explains what is returned and how to interpret it.

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 long but each sentence adds value. It is well-structured with clear sections. Could be slightly more concise, but the complexity justifies the length.

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?

Despite no output schema, the description thoroughly explains the return fields (discount, fair-value, confidence, comps) and the tool's behavior (filters, limitations, fallback). It is complete for the tool's purpose.

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?

While schema covers 100% of parameters, the description adds crucial context like 'minConfidence floored at MEDIUM', 'maxLength omit for no cap', and explains default values and rationale. This adds significant value 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 scans the ENS marketplace for undervalued names, returning ranked opportunities with discount and fair-value metrics. It distinguishes itself from sibling tools like get_valuation and search_ens_names by explicitly stating when to use which.

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 when-to-use (user asks for 'best value', 'bargains', etc.) and when-not-to-use (specific collections) with alternatives (search_ens_names, sweep). Also mentions filters for narrow queries.

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.