| 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:
-
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)
-
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
-
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
