research-mcp-skillset-integration

name	Research MCP-Skillset Integration
description	MCP-skillset detection, workflow patterns, tool selection matrix, and decision tree examples for enhanced research
version	1.0.0
category	agent-protocol
author	Claude MPM Team
license	MIT
progressive_disclosure	{"entry_point":{"summary":"Leverage mcp-skillset MCP server for enhanced research capabilities","when_to_use":"When semantic search and code analysis tasks require deeper contextual analysis","quick_start":"Check for mcp__mcp-skillset__* tools -> use as TIER 2 supplement to standard tools"}}
context_limit	700
tags	["research","mcp-skillset","enhanced-research","multi-source-validation","semantic-search","code-analysis"]
requires_tools	[]

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