Product Name Scout Agent
Learn how to build an AI agent that provides comprehensive product name research and brand validation capabilities. This tutorial shows how to create an agent that can analyze potential product names through search engine analysis, developer platform collision detection, and automated scoring algorithms.
🎥 Demo Video
Watch the Product Name Scout Agent in action:
What You'll Build
A product name research agent that can:
- Check for domain availability (.com, .ai, .dev, .io, etc.) for your product name
- Analyze search engine results for brand competition across Google, DuckDuckGo, and Brave
- Check autocomplete suggestions to identify spelling and recognition issues
- Detect conflicts on developer platforms (GitHub, npm, PyPI)
- Conduct competitive research and trademark conflict assessment
- Provide a final 1-100 scoring based on all of these factors
Understanding the Architecture
The product name scout agent follows Saiki's framework design with clear separation of responsibilities:
- MCP Servers with tools: Multiple specialized servers for different aspects of name research. These handle specific research tasks (SERP analysis, domain checking, etc.)
- Agent: Orchestrates complex research workflows and synthesizes findings
This architecture allows the agent to conduct thorough research while maintaining clear, actionable insights.
MCP Server Code
The core functionality is provided by three MCP servers. To understand the complete MCP server implementations, refer to the mcp-servers repository:
- Product Name Scout Server: src/product-name-scout - SERP analysis, autocomplete, dev collisions, and scoring
- Domain Checker Server: src/domain-checker - Domain availability checking via WHOIS and DNS
- DuckDuckGo Server: External third-party server for web search capabilities
Step 1: Setting Up the Project
The product name research agent uses multiple MCP servers for comprehensive analysis:
agents/product-name-researcher/
├── product-name-researcher.yml # Agent configuration
└── README.md # Documentation
Step 2: Quick Setup
The agent uses published MCP servers that are automatically installed:
# From the saiki project root
saiki --agent agents/product-name-researcher/product-name-researcher.yml
The agent will automatically download and install all required MCP servers:
truffle-ai-domain-checker-mcp
- Domain availability checkingduckduckgo-mcp-server
- Web search and competitive research@truffle-ai/product-name-scout-mcp
- Advanced name analysis tools
Step 3: Understanding the Agent Configuration
The agent is configured in product-name-researcher.yml
:
systemPrompt: |
You are a specialized Product Name Research Agent focused on helping entrepreneurs,
product managers, and marketing teams validate potential product names through
comprehensive research. Your expertise combines domain availability checking with
competitive landscape analysis and advanced searchability assessment.
mcpServers:
# Domain availability checking
domain-checker:
type: stdio
command: uvx
args:
- truffle-ai-domain-checker-mcp
# Web search for competitive research
duckduckgo:
type: stdio
command: uvx
args:
- duckduckgo-mcp-server
# Advanced product name analysis
product-name-scout:
type: stdio
command: npx
args:
- "@truffle-ai/product-name-scout-mcp"
llm:
provider: anthropic
model: claude-4-sonnet-20250514
apiKey: $ANTHROPIC_API_KEY
toolConfirmation:
mode: auto-approve
Key Components Explained
- systemPrompt: Defines specialized product name research expertise
- mcpServers: Connects to three complementary research tools
- toolConfirmation: Auto-approves tools for seamless research workflow
- llm: Configures the language model for intelligent analysis
Step 4: Available Tools
The product name scout agent provides 9 specialized research tools across three categories:
Domain Research Tools (3)
check_domain
- Check availability of a single domaincheck_multiple_domains
- Check multiple domains simultaneouslycheck_domain_variations
- Check a base name across multiple TLD extensions
Advanced Name Analysis Tools (4)
check_brand_serp
- Analyze search engine results for brand competitionget_autocomplete
- Get search engine autocomplete suggestionscheck_dev_collisions
- Check for existing projects on GitHub, npm, PyPIscore_name
- Comprehensive scoring across multiple brand viability factors
Competitive Research Tools (2)
search
- DuckDuckGo search for competitive analysis and market researchget_content
- Extract and analyze content from specific web pages
Step 5: Research Methodology
The agent follows a systematic approach to product name validation:
For Single Name Research:
- Domain Availability Check: Verify availability across key TLDs (.com, .io, .app, etc.)
- SERP Competition Analysis: Assess existing brand presence in search results
- Autocomplete Pattern Analysis: Understand search behavior and spelling issues
- Developer Platform Conflicts: Check for existing projects on GitHub, npm, PyPI
- Competitive Research: Search for existing companies/products with similar names
- Trademark Assessment: Search for trademark conflicts and legal issues
- Comprehensive Scoring: Generate overall viability score with detailed breakdown
For Multiple Name Comparison:
- Batch Domain Analysis: Check all names across key TLD extensions
- Parallel Research: Conduct SERP and collision analysis for each name
- Comparison Matrix: Create comprehensive comparison including all factors
- Scoring & Ranking: Rank names based on availability, conflicts, and strategic value
- Final Recommendation: Provide clear recommendation with detailed reasoning
Step 6: Running the Agent
Start the product name research agent:
# From the project root
saiki --agent agents/product-name-researcher/product-name-researcher.yml
Step 7: Testing with Example Prompts
Let's test the agent with realistic product name research scenarios:
Basic Name Validation
"I'm considering 'ZenFlow' as a product name for a productivity app. Can you research this name?"
What happens: The agent orchestrates a complete research workflow:
- Checks domain availability for zenflow.com, zenflow.io, etc.
- Analyzes search competition for "ZenFlow"
- Checks autocomplete suggestions
- Searches GitHub, npm, and PyPI for conflicts
- Provides comprehensive scoring and recommendations
Domain-Focused Research
"Check domain availability for 'CodeCraft' across all major extensions"
What happens: The agent calls check_domain_variations
to check .com, .net, .org, .io, .app, .dev, and .tech extensions simultaneously.
Competitive Analysis
"Research existing companies using the name 'DataFlow' and assess trademark risks"
What happens: The agent combines multiple tools:
search
for competitive landscape analysischeck_brand_serp
for search presence assessmentget_content
to analyze competitor websites- Synthesizes findings into trademark risk assessment
Multiple Name Comparison
"Compare these three product names for a fintech startup: 'PayEase', 'CashFlow', and 'MoneyBridge'"
What happens: The agent conducts parallel research on all three names and provides a ranked comparison with detailed scoring breakdown.
Developer-Focused Research
"I'm launching a new JavaScript library. Check if 'ReactFlow' conflicts with existing packages"
What happens: The agent focuses on check_dev_collisions
for GitHub and npm, plus domain research for library website.
Step 8: Understanding the Scoring System
The score_name
tool uses weighted scoring across multiple factors:
Scoring Factors (with weights):
- SERP Emptiness (3x): Lower search competition = higher score
- Spellability (2x): Easy to spell and remember names score higher
- Autocomplete Behavior (2x): Clean autocomplete patterns score higher
- Developer Collisions (2x): Fewer conflicts on dev platforms = higher score
- Domain Availability (3x): More available domains = higher score
- Trademark Conflicts (2x): Lower trademark risk = higher score
- International Considerations (1x): Global usability assessment
Score Interpretation:
- 80-100: Excellent name with strong availability and low conflicts
- 60-79: Good name with minor issues to consider
- 40-59: Moderate concerns requiring careful evaluation
- 20-39: Significant conflicts or availability issues
- 0-19: High-risk name with major obstacles
Step 9: Example Research Workflow
Here's a complete workflow for researching a product name:
User: "Research 'CloudSync' for a file synchronization service"
Agent Response:
"I'll conduct comprehensive research on 'CloudSync' for your file synchronization service. Let me analyze multiple factors:
🔍 DOMAIN AVAILABILITY
[Checks cloudsync.com, cloudsync.io, cloudsync.app, etc.]
- cloudsync.com: ❌ NOT AVAILABLE
- cloudsync.io: ✅ AVAILABLE
- cloudsync.app: ✅ AVAILABLE
🔍 SEARCH COMPETITION ANALYSIS
[Analyzes Google search results for 'CloudSync']
- Found ~45,000 results
- Existing competitors in cloud storage space
- Mixed signals on brand conflicts
🔍 DEVELOPER PLATFORM CHECK
[Searches GitHub, npm, PyPI for conflicts]
- GitHub: 12 repositories with 'cloudsync'
- npm: 3 packages with similar names
- PyPI: 1 existing package
🔍 AUTOCOMPLETE ANALYSIS
[Checks search suggestions]
- Google suggests: 'cloudsync app', 'cloudsync software'
- Shows some existing recognition
📊 COMPREHENSIVE SCORE: 67/100
- Moderate availability with some conflicts
- Strong domain options available (.io, .app)
- Consider alternative: 'SyncCloud' or 'CloudFlow'
💡 RECOMMENDATION: Proceed with caution. While .io and .app domains are available, existing competition suggests trademark research needed. Consider variants like 'SyncStream' or 'CloudVault' for cleaner positioning."
Research Best Practices
Competitive Research Guidelines:
- Search for exact name matches and close variations
- Research across different industries and markets
- Look for existing trademarks and brand registrations
- Check social media presence and brand mentions
- Consider international markets and global brand presence
Search Strategy Guidelines:
- Use specific queries: "[name] company", "[name] trademark", "[name] brand"
- Search industry-specific usage: "[name] [industry]", "[name] product"
- Look for legal conflicts: "[name] lawsuit", "[name] trademark dispute"
- Research naming trends in the target industry
Common Use Cases
- Startup Name Validation: Complete due diligence for new company names
- Product Launch Research: Validate product names before market entry
- Brand Extension Analysis: Research names for new product lines
- Trademark Risk Assessment: Identify potential legal conflicts early
- Domain Strategy Planning: Optimize domain portfolio decisions
- Competitive Intelligence: Understand market landscape and positioning
Ready for comprehensive name research? Start the agent and begin validating your product names with professional-grade analysis tools!