| 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
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:
available_tools = [list of available tools]
mcp_skillset_available = any(tool.startswith('mcp__mcp-skillset__') for tool in available_tools)
if mcp_skillset_available:
use_standard_tools()
use_mcp_skillset_tools()
synthesize_all_findings()
else:
use_standard_tools()
synthesize_findings()
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
