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Nombrio Clearance Check

Clearance screen — 2 to 8 names at once (free)

check_names
Read-only

Screen 2-8 candidate names in ONE call and get them ranked by viability (best first). Runs the same live free Clearance Check as check_name for every name in parallel — US trademark verdict, count-only EU trademark screen, domains with prices, social handles, developer namespaces, iOS App Store — and returns a ranked summary, per-name verdicts and full-result links. Use it whenever the user is brainstorming, has a list, or asks which of several names is available or safest — including shortlists of project, package, repo, app or domain names that come up mid-development: one check_names call replaces many check_name calls and finishes in about the time of one. Each name counts as one check against the free fair-use pool (per-caller daily allowance). Not legal advice. Data by nombrio.com.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesYes2-8 candidate names, e.g. ["Lumeo", "Brewly", "Northwind"]. Plain names only — no URLs, no TLDs, no @handles. Duplicates are checked once.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
planNo
linksYes
quotaNoPer-caller fair-use quota. Absent on cache hits and degraded paths.
checkedYesNames that received a full free-tier answer.
rankingYesNames best → worst by viability band; unchecked names last.
resultsYes
summaryYes
requestedYes
disclaimerYes

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the read-only and open-world annotations, the description reveals meaningful behavior: parallel execution, the exact data categories screened, the ranked summary/per-name verdicts/link outputs, and the fair-use pool cost per name. The disclaimer 'Not legal advice' and data source 'nombrio.com' add useful context. Nothing contradicts the 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?

The first sentence is front-loaded with the core action and result, and the rest is information-dense. It is somewhat long as a single paragraph with embedded lists, but every sentence serves a purpose (result, data coverage, usage, cost, disclaimer).

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 batched screening tool, the description is complete: it states what data sources are checked, how results are delivered, the per-name pool cost, and the legal caveat. The output schema exists, so not detailing return values is acceptable; only the exact daily allowance is unspecified, but that is dynamic and does not block correct invocation.

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?

The schema already covers 100% of parameter semantics with constraints and examples, so the baseline is 3. The description reinforces that names are candidates and each name is checked, but does not add parameter-level 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 opens with a specific verb and resource: 'Screen 2-8 candidate names in ONE call' and states the result ('ranked by viability, best first'). It also distinguishes itself from the sibling check_name by emphasizing the batch (2-8) and parallel execution.

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?

It explicitly says when to use it: 'whenever the user is brainstorming, has a list, or asks which of several names is available or safest' and lists mid-development shortlist contexts. It also routes away from the single-name sibling by saying 'one check_names call replaces many check_name calls.'

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

A4.5/5.0
Disambiguation5/5

Each tool has a clearly separated responsibility: single-name clearance, multi-name ranked screening, starting a naming session, and polling that session. The one-vs-many and start-vs-get distinctions are explicit and cross-referenced, so an agent is unlikely to pick the wrong tool.

Naming Consistency5/5

All tool names follow the same snake_case verb_noun pattern: check_name, check_names, start_naming_session, get_naming_session. The pluralization and start/get pairs are semantically predictable.

Tool Count5/5

Four tools is a tight, appropriate count for the server's purpose: two cover the free clearance-check workflow and two cover the async naming-session workflow. No tool feels redundant, and none is missing from the core set.

Completeness4/5

The primary workflows are complete: one-name checks, batch checks with ranking, and starting/polling naming sessions. Minor gaps remain, such as no way to check the daily fair-use allowance and slightly inconsistent UK trademark coverage between the free and paid descriptions.

Resources