Dappier MCP vs Domain Search MCP

search_domain

Search for domain availability and pricing across multiple TLDs. Returns: - Availability status for each domain - Pricing (first year and renewal) when Pricing API is configured - Whether WHOIS privacy is included - Human-readable insights and next steps Examples: - search_domain("vibecoding") → checks vibecoding.com, .io, .dev - search_domain("myapp", ["com", "io"]) → checks specific TLDs

bulk_search

Check availability for multiple domain names at once. Efficiently searches up to 100 domains in parallel with rate limiting. Use a single TLD for best performance. Returns: - Availability status for each domain - Pricing where available - Summary statistics Example: - bulk_search(["vibecoding", "myapp", "coolstartup"], "io")

compare_registrars

Compare domain pricing across multiple registrars. Checks the same domain at different registrars to find: - Best first year price - Best renewal price - Overall recommendation Uses the Pricing API when configured; otherwise falls back to BYOK registrars. Returns pricing comparison and a recommendation. Example: - compare_registrars("vibecoding", "com") → compares Porkbun (and any configured BYOK registrars)

suggest_domains

Generate and check availability of domain name variations. Creates variations like: - Hyphenated: vibe-coding - With numbers: vibecoding1, vibecoding2 - Prefixes: getvibecoding, tryvibecoding - Suffixes: vibecodingapp, vibecodinghq Returns only available suggestions, ranked by quality. Example: - suggest_domains("vibecoding") → finds available variations

suggest_domains_smart

AI-powered domain name suggestion engine. Generate creative, brandable domain names from keywords or business descriptions. Combines multiple intelligent sources for maximum coverage and quality. Features: - ZERO-CONFIG: Works out of the box with our fine-tuned Qwen 7B-DPO model - Domain-specialized fine-tuned model for higher quality suggestions - Understands natural language queries ("coffee shop in seattle") - Auto-detects industry for contextual suggestions - Generates portmanteau/blended names (instagram = instant + telegram) - Applies modern naming patterns (ly, ify, io, hub, etc.) - Filters premium domains by default - Availability verified via Porkbun/RDAP - Graceful fallback: Fine-tuned Qwen → Semantic engine Examples: - suggest_domains_smart("ai customer service") → AI-themed suggestions - suggest_domains_smart("organic coffee", industry="food") → Food-focused names - suggest_domains_smart("vibecoding", style="short") → Minimal length names

tld_info

Get information about a Top Level Domain (TLD). Returns: - Description and typical use case - Price range - Any special restrictions - Popularity and recommendations Example: - tld_info("io") → info about .io domains

check_socials

Check if a username is available on social media and developer platforms. Supports 10 platforms with varying confidence levels: - HIGH: GitHub, npm, PyPI, Reddit, Twitter/X (reliable public APIs) - MEDIUM: YouTube, ProductHunt (status code based) - LOW: Instagram, LinkedIn, TikTok (block automated checks - verify manually) Returns availability status with confidence indicator. Example: - check_socials("vibecoding") → checks GitHub, Twitter, Reddit, npm - check_socials("myapp", ["github", "npm", "pypi"]) → developer platforms only

analyze_project

Analyze a local project or GitHub repository to extract context for domain suggestions. This tool scans project manifest files (package.json, pyproject.toml, Cargo.toml, go.mod) and README files to understand your project, then generates relevant domain name suggestions. **Use Cases:** 1. Find a domain for your existing codebase 2. Get domain ideas that match your project's identity 3. Analyze any GitHub repo for branding inspiration **Supported Projects:** - Node.js (package.json) - Python (pyproject.toml, setup.py) - Rust (Cargo.toml) - Go (go.mod) - Any project with README.md **Examples:** - analyze_project("/path/to/my-app") → Analyzes local project - analyze_project("https://github.com/vercel/next.js") → Analyzes GitHub repo - analyze_project("/my-project", suggest_domains=true, style="short") → Short brandable names

hunt_domains

Find valuable domains for investment opportunities. Scans Sedo auctions, generates pattern-based candidates, and calculates investment scores based on length, TLD value, keyword matches, and pronounceability. **Investment Score Factors:** - Length: Shorter = higher score (3-4 chars = +25 points) - TLD Value: .com = +25, .io/.ai = +15, .co = +12 - Keyword Match: +5 per keyword found in domain - Pronounceability: Good vowel ratio = +10 - Aftermarket Price: Lower prices get bonus points **Pattern Types:** - `short`: 3-5 character pronounceable patterns (CVC, CVCV) - `dictionary`: Common words that make good domains - `brandable`: Keyword + modern suffixes (-ly, -ify, -io) - `acronym`: Abbreviations from multiple keywords - `numeric`: Keyword + numbers (7, 24, 365) **Examples:** - hunt_domains(keywords=["ai", "chat"]) → Find AI/chat themed domains - hunt_domains(max_length=5, tlds=["com"]) → Ultra-short .com domains - hunt_domains(max_aftermarket_price=500) → Sedo auctions under $500 - hunt_domains(patterns=["short"], score_threshold=60) → High-scoring short domains

expiring_domains

Find domains that are about to expire and may become available soon. Monitors the federated negative cache for domains approaching their expiration date. Useful for domain investors and those watching specific domains. Requires NEGATIVE_CACHE_URL to be configured. Examples: - expiring_domains(days=30) → Domains expiring in the next 30 days - expiring_domains(tlds=["com"], days=7) → .com domains expiring within a week - expiring_domains(keywords="ai") → AI-related domains expiring soon

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