Research MCP-Skillset Integration

MCP-Skillset Integration (Optional Enhancement)

When conducting research, you can leverage additional skill-based research capabilities if mcp-skillset MCP server is installed and available. This is an OPTIONAL enhancement that supplements (not replaces) your standard research tools.

Detection

Check for mcp-skillset tools by looking for tools with the prefix: mcp__mcp-skillset__*

Common mcp-skillset tools that enhance research capabilities:

• mcp__mcp-skillset__web_search - Enhanced web search with contextual understanding
• mcp__mcp-skillset__code_analysis - Deep code pattern analysis and architectural insights
• mcp__mcp-skillset__documentation_lookup - API and library documentation search
• mcp__mcp-skillset__best_practices - Industry best practices and standards research
• mcp__mcp-skillset__technology_research - Technology evaluation and comparison analysis
• mcp__mcp-skillset__security_analysis - Security patterns and vulnerability research

Research Workflow with MCP-Skillset

When mcp-skillset tools are available, enhance your research process:

1. Primary Research Layer (Always executed - standard tools):

   • Use Glob for file pattern discovery
   • Use Grep for code content search
   • Use Read for file analysis (with memory limits)
   • Use WebSearch for general web queries
   • Use WebFetch for fetching and analyzing web pages
   • Use mcp-vector-search for semantic code search (if available)

2. Enhanced Research Layer (Optional - if mcp-skillset available):

   • Use mcp-skillset tools for deeper contextual analysis
   • Cross-reference findings between standard and skillset tools
   • Leverage skill-specific expertise for specialized research
   • Combine multiple perspectives for richer insights

3. Synthesis (Comprehensive analysis):

   • Integrate findings from all available sources
   • Identify patterns across different tool outputs
   • Provide multi-dimensional analysis with confidence levels
   • Document which tools contributed to each finding

Example Research Decision Trees

Example 1: Authentication Best Practices Research

User Request: "Research authentication best practices for Node.js"

Standard Approach (Always executed):
|- WebSearch: "Node.js authentication best practices 2025"
|- Grep: Search codebase for existing auth patterns
|- Read: Review authentication middleware files
'- Synthesize: Compile findings into recommendations

Enhanced with mcp-skillset (if available):
|- WebSearch: "Node.js authentication best practices 2025"
|- mcp__mcp-skillset__best_practices: "Node.js authentication security"
|- Grep: Search codebase for existing auth patterns
|- mcp__mcp-skillset__code_analysis: Analyze auth pattern implementations
|- Read: Review authentication middleware files
|- mcp__mcp-skillset__security_analysis: "JWT token security Node.js"
'- Synthesize: Combine findings from 6 sources for comprehensive analysis

Result: Richer analysis with industry standards, security insights, and code patterns

Example 2: Technology Stack Evaluation

User Request: "Evaluate database options for high-throughput API"

Standard Approach (Always executed):
|- WebSearch: "database comparison high throughput API"
|- WebFetch: Fetch benchmark articles and comparisons
|- Grep: Check existing database usage in codebase
'- Synthesize: Present options with trade-offs

Enhanced with mcp-skillset (if available):
|- WebSearch: "database comparison high throughput API"
|- mcp__mcp-skillset__technology_research: "PostgreSQL vs MongoDB throughput"
|- WebFetch: Fetch benchmark articles and comparisons
|- mcp__mcp-skillset__best_practices: "database selection criteria"
|- Grep: Check existing database usage in codebase
|- mcp__mcp-skillset__code_analysis: Analyze current data access patterns
'- Synthesize: Multi-source analysis with benchmark data and best practices

Result: Data-driven recommendations with industry context and codebase analysis

Example 3: API Documentation Research

User Request: "Find documentation for Stripe payment intents API"

Standard Approach (Always executed):
|- WebSearch: "Stripe payment intents API documentation"
|- WebFetch: https://stripe.com/docs/api/payment_intents
'- Summarize: Key endpoints and usage patterns

Enhanced with mcp-skillset (if available):
|- WebSearch: "Stripe payment intents API documentation"
|- mcp__mcp-skillset__documentation_lookup: "Stripe payment intents"
|- WebFetch: https://stripe.com/docs/api/payment_intents
|- mcp__mcp-skillset__code_analysis: Find Stripe usage in codebase
'- Synthesize: Documentation + existing implementation patterns + examples

Result: Complete picture of API capabilities and current usage in project

Integration Guidelines

DO:

• Check if mcp-skillset tools are available before attempting to use them
• Use mcp-skillset as supplementary research (not a replacement for standard tools)
• Combine findings from standard tools AND mcp-skillset for richer analysis
• Fall back gracefully to standard tools if mcp-skillset is unavailable
• Document which tools contributed to each finding in your analysis
• Leverage mcp-skillset for specialized domains (security, best practices, etc.)
• Cross-validate findings between different tool sources

DON'T:

• Require mcp-skillset tools (they are optional enhancements)
• Block or fail research if mcp-skillset tools are not available
• Replace standard research tools entirely with mcp-skillset
• Assume mcp-skillset is always installed or available
• Provide error messages or warnings if mcp-skillset is unavailable
• Skip standard research steps when mcp-skillset is available
• Use mcp-skillset without first executing standard research approaches

Tool Selection Strategy

TIER 1: Standard Tools (Always Use - Foundation)

• Glob: File pattern matching and discovery
• Grep: Code content search with regex patterns
• Read: Direct file reading (with memory management)
• WebSearch: General web search queries
• WebFetch: Fetch and analyze web content
• mcp-vector-search: Semantic code search (if available)

TIER 2: Enhanced Tools (Use When Available - Supplementary)

• mcp__mcp-skillset__web_search: Context-aware web research
• mcp__mcp-skillset__code_analysis: Deep architectural analysis
• mcp__mcp-skillset__documentation_lookup: API/library documentation
• mcp__mcp-skillset__best_practices: Industry standards and patterns
• mcp__mcp-skillset__security_analysis: Security vulnerability research
• mcp__mcp-skillset__technology_research: Technology evaluation and comparison

Selection Decision Matrix

Research Task Type          | Standard Tools              | +mcp-skillset Enhancement
---------------------------|----------------------------|---------------------------
Code Pattern Search        | Grep, mcp-vector-search    | +code_analysis
Architectural Analysis     | Read, Glob, Grep           | +code_analysis
Best Practices Research    | WebSearch, WebFetch        | +best_practices
Security Evaluation        | Grep (vulnerabilities)     | +security_analysis
API Documentation          | WebSearch, WebFetch        | +documentation_lookup
Technology Comparison      | WebSearch, WebFetch        | +technology_research
Industry Standards         | WebSearch                  | +best_practices
Performance Analysis       | Grep, Read                 | +code_analysis

Availability Check Pattern

Before using mcp-skillset tools, verify availability in your tool set:

# Conceptual pattern (not literal code)
available_tools = [list of available tools]
mcp_skillset_available = any(tool.startswith('mcp__mcp-skillset__') for tool in available_tools)

if mcp_skillset_available:
    # Enhanced research workflow with skillset tools
    use_standard_tools()
    use_mcp_skillset_tools()  # Supplementary layer
    synthesize_all_findings()
else:
    # Standard research workflow only
    use_standard_tools()
    synthesize_findings()
    # No error/warning needed - optional enhancement

Research Quality with MCP-Skillset

When mcp-skillset is available, enhance research quality by:

• Multi-Source Validation: Cross-reference findings from 4-6 sources instead of 2-3
• Deeper Context: Leverage skill-specific expertise for specialized domains
• Richer Insights: Combine code analysis with best practices and documentation
• Higher Confidence: Validate patterns across multiple analytical perspectives
• Comprehensive Coverage: Standard tools provide breadth, skillset adds depth

Graceful Degradation

If mcp-skillset tools are not available:

• Proceed with standard research tools without any interruption
• Maintain same research methodology and quality standards
• No need to inform user about unavailable optional enhancements
• Continue to deliver comprehensive analysis using available tools
• Research quality remains high with standard tool suite