LABSCO SUMMARY

Naming and availability in one loop, so a good name is not lost at the checkout page.

The two-phase design is the interesting part: it does not generate names, your model does — this supplies the lane prompts, then scores what comes back and checks what survives. Premium and auction domains are flagged rather than reported as free, which is the failure mode that wastes the most time. Treat the scores as rankings for comparing candidates against each other, and the availability results as one source at one moment — worth re-checking before you register anything.

What it is

A naming engine with availability checking behind it. name_project hands your own model the generation instructions, scores what comes back for AI-slop patterns, and checks the survivors against domains, social platforms and npm. The other tools are that availability layer on its own — multi-TLD lookups, bulk checks, registrar price comparison, TLD metadata.

What you get

A two-phase naming run: phase one returns lane-by-lane generation instructions for your model, phase two scores and ranks the candidates and checks availability for the top 12
Four naming modes — from a brief, from the current workspace, from a name you already like, or fitted to a domain you found
Anti-slop scoring that names its reason, such as an overused nex- prefix or -ify suffix
One name checked across .com, .io, .dev, .ai and hundreds of other TLDs, with premium and auction domains flagged rather than reported as free
Up to 100 names checked against a single TLD in one parallel call
Registrar pricing compared for first year and renewal, each result carrying a price_check_url so the final number is confirmed at checkout
Username availability across GitHub, npm, PyPI, Reddit, Twitter and other platforms, each with its confidence level stated
Domain variations generated mechanically — hyphenated, numbered, prefixed — or as brandable names from a description
A local project or GitHub repository scanned for context, then names suggested from what it found
Aftermarket signals on taken domains: Sedo auction hints and nameserver-based marketplace hints
TLD metadata: what it is for, its price range, its restrictions
Investment scans: pattern-generated candidates scored, and domains approaching expiry

Requirements

npx -y domain-search-mcp@latest over stdio, or --http for a server on MCP_PORT 3000 that also exposes REST endpoints and an OpenAPI 3.1 spec for ChatGPT Actions. Availability needs no keys at all: RDAP first, GoDaddy for premium and auction detection, WHOIS as the last resort. Pricing is the part that needs setting up — point PRICING_API_BASE_URL at a backend that holds registrar keys, or bring your own with PORKBUN_API_KEY and PORKBUN_API_SECRET, or NAMECHEAP_API_KEY, NAMECHEAP_API_USER and NAMECHEAP_CLIENT_IP. SLIM_TOOLS=true cuts the surface to six tools; REDIS_URL shares the cache across instances; QWEN_INFERENCE_ENDPOINT points brandable suggestions at your own inference server, with an offline semantic engine as the fallback when it is unset.

Setup effort

One command — npx -y domain-search-mcp@latest

See installation options

USE CASES

01

Naming a project and finding out what is actually free

Ask for generation instructions, feed your candidates back, and get them scored and checked against domains, socials and npm in one response.

name_project

02

Checking one name across the TLDs you would consider

Search .com, .io, .dev and the rest at once, with premium and auction domains marked as such.

search_domain

03

Screening a long shortlist quickly

Check up to 100 names against a single TLD in one parallel call rather than one at a time.

bulk_search

04

Making sure the handle is free too

Check the username across GitHub, npm, PyPI, Reddit and the rest, each answer carrying its confidence level.

check_socials

05

Not being surprised by the renewal price

Compare the same domain across registrars for first-year and renewal cost, then confirm at the checkout link.

compare_registrarstld_info

06

Naming something from the code that already exists

Scan the local project or GitHub repository for context, then get suggestions built out of it.

analyze_projectsuggest_domains_smart

07

Hunting for a domain worth buying

Scan auctions and pattern-generated candidates with investment scores, and watch what is about to expire.

hunt_domainsexpiring_domains

12 tools

Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.

`ai_health`

Check health status of AI inference services.

Returns status of:

VPS Qwen (self-hosted llama.cpp)
Together.ai (cloud fallback)
Semantic Engine (offline, always available)
Circuit breaker states
Adaptive concurrency limits

Use when:

AI suggestions are slow or failing
Diagnosing which AI source is being used
Monitoring inference infrastructure

BEFORE YOU INSTALL

What to know first

Tools you get

12 tools when we connected. SLIM_TOOLS=true cuts the set to six, and the project warns a future 2.0 release may make that the default. ADVANCED_TOOLS=true is a deprecated alias with no effect today.

Requires

npx on PATH

Where it runs

Both documented clients run the server on your machine.

CONTEXT COST

Light Standing cost

Rated by the size of the 12 tool definitions that ride along on every turn, not by how many there are

INSTALLATION

Connect your client

Server setup

TERMINAL

npx -y domain-search-mcp@latest

MCP Clients

Claude Code

CLAUDE CODE · JSON

{
  "mcpServers": {
    "domain-search": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "domain-search-mcp@latest"
      ]
    }
  }
}

Claude Desktop

CLAUDE DESKTOP · JSON

{
  "mcpServers": {
    "domain-search": {
      "command": "npx",
      "args": [
        "-y",
        "domain-search-mcp@latest"
      ]
    }
  }
}

Domain Search MCP

Naming and availability in one loop, so a good name is not lost at the checkout page.

What it is

What you get

Requirements

Setup effort

Naming a project and finding out what is actually free

Checking one name across the TLDs you would consider

Screening a long shortlist quickly

Making sure the handle is free too

Not being surprised by the renewal price

Naming something from the code that already exists

Hunting for a domain worth buying

12 tools

`ai_health`

`analyze_project`

`bulk_search`

`check_socials`

`compare_registrars`

`expiring_domains`

`hunt_domains`

`name_project`

`search_domain`

`suggest_domains`

`suggest_domains_smart`

`tld_info`

What to know first

Tools you get

Requires

Where it runs

Light Standing cost

Connect your client

Server setup

MCP Clients

Claude Code

Claude Desktop

Domain Search MCP

Naming and availability in one loop, so a good name is not lost at the checkout page.

What it is

What you get

Requirements

Setup effort

Naming a project and finding out what is actually free

Checking one name across the TLDs you would consider

Screening a long shortlist quickly

Making sure the handle is free too

Not being surprised by the renewal price

Naming something from the code that already exists

Hunting for a domain worth buying

12 tools

ai_health

analyze_project

bulk_search

check_socials

compare_registrars

expiring_domains

hunt_domains

name_project

search_domain

suggest_domains

suggest_domains_smart

tld_info

What to know first

Tools you get

Requires

Where it runs

Light Standing cost

Connect your client

Server setup

MCP Clients

Claude Code

Claude Desktop

Same category

Domain Radar

InstaDomain

MCP Domain Availability Checker

Internet-Names-MCP

`ai_health`

`analyze_project`

`bulk_search`

`check_socials`

`compare_registrars`

`expiring_domains`

`hunt_domains`

`name_project`

`search_domain`

`suggest_domains`

`suggest_domains_smart`

`tld_info`