refactor: PEP8 compliance - directory rename and code formatting (#425)

* fix(orchestration): add WebFetch auto-trigger for infrastructure configuration

Problem: Infrastructure configuration changes (e.g., Traefik port settings)
were being made based on assumptions without consulting official documentation,
violating the 'Evidence > assumptions' principle in PRINCIPLES.md.

Solution:
- Added Infrastructure Configuration Validation section to MODE_Orchestration.md
- Auto-triggers WebFetch for infrastructure tools (Traefik, nginx, Docker, etc.)
- Enforces MODE_DeepResearch activation for investigation
- BLOCKS assumption-based configuration changes

Testing: Verified WebFetch successfully retrieves Traefik official docs (port 80 default)

This prevents production outages from infrastructure misconfiguration by ensuring
all technical recommendations are backed by official documentation.

* feat: Add PM Agent (Project Manager Agent) for seamless orchestration

Introduces PM Agent as the default orchestration layer that coordinates
all sub-agents and manages workflows automatically.

Key Features:
- Default orchestration: All user interactions handled by PM Agent
- Auto-delegation: Intelligent sub-agent selection based on task analysis
- Docker Gateway integration: Zero-token baseline with dynamic MCP loading
- Self-improvement loop: Automatic documentation of patterns and mistakes
- Optional override: Users can specify sub-agents explicitly if desired

Architecture:
- Agent spec: SuperClaude/Agents/pm-agent.md
- Command: SuperClaude/Commands/pm.md
- Updated docs: README.md (15→16 agents), agents.md (new Orchestration category)

User Experience:
- Default: PM Agent handles everything (seamless, no manual routing)
- Optional: Explicit --agent flag for direct sub-agent access
- Both modes available simultaneously (no user downside)

Implementation Status:
-  Specification complete
-  Documentation complete
-  Prototype implementation needed
-  Docker Gateway integration needed
-  Testing and validation needed

Refs: kazukinakai/docker-mcp-gateway (IRIS MCP Gateway integration)

* feat: Add Agent Orchestration rules for PM Agent default activation

Implements PM Agent as the default orchestration layer in RULES.md.

Key Changes:
- New 'Agent Orchestration' section (CRITICAL priority)
- PM Agent receives ALL user requests by default
- Manual override with @agent-[name] bypasses PM Agent
- Agent Selection Priority clearly defined:
  1. Manual override → Direct routing
  2. Default → PM Agent → Auto-delegation
  3. Delegation based on keywords, file types, complexity, context

User Experience:
- Default: PM Agent handles everything (seamless)
- Override: @agent-[name] for direct specialist access
- Transparent: PM Agent reports delegation decisions

This establishes PM Agent as the orchestration layer while
respecting existing auto-activation patterns and manual overrides.

Next Steps:
- Local testing in agiletec project
- Iteration based on actual behavior
- Documentation updates as needed

* refactor(pm-agent): redesign as self-improvement meta-layer

Problem Resolution:
PM Agent's initial design competed with existing auto-activation for task routing,
creating confusion about orchestration responsibilities and adding unnecessary complexity.

Design Change:
Redefined PM Agent as a meta-layer agent that operates AFTER specialist agents
complete tasks, focusing on:
- Post-implementation documentation and pattern recording
- Immediate mistake analysis with prevention checklists
- Monthly documentation maintenance and noise reduction
- Pattern extraction and knowledge synthesis

Two-Layer Orchestration System:
1. Task Execution Layer: Existing auto-activation handles task routing (unchanged)
2. Self-Improvement Layer: PM Agent meta-layer handles documentation (new)

Files Modified:
- SuperClaude/Agents/pm-agent.md: Complete rewrite with meta-layer design
  - Category: orchestration → meta
  - Triggers: All user interactions → Post-implementation, mistakes, monthly
  - Behavioral Mindset: Continuous learning system
  - Self-Improvement Workflow: BEFORE/DURING/AFTER/MISTAKE RECOVERY/MAINTENANCE

- SuperClaude/Core/RULES.md: Agent Orchestration section updated
  - Split into Task Execution Layer + Self-Improvement Layer
  - Added orchestration flow diagram
  - Clarified PM Agent activates AFTER task completion

- README.md: Updated PM Agent description
  - "orchestrates all interactions" → "ensures continuous learning"

- Docs/User-Guide/agents.md: PM Agent section rewritten
  - Section: Orchestration Agent → Meta-Layer Agent
  - Expertise: Project orchestration → Self-improvement workflow executor
  - Examples: Task coordination → Post-implementation documentation

- PR_DOCUMENTATION.md: Comprehensive PR documentation added
  - Summary, motivation, changes, testing, breaking changes
  - Two-layer orchestration system diagram
  - Verification checklist

Integration Validated:
Tested with agiletec project's self-improvement-workflow.md:
 PM Agent aligns with existing BEFORE/DURING/AFTER/MISTAKE RECOVERY phases
 Complements (not competes with) existing workflow
 agiletec workflow defines WHAT, PM Agent defines WHO executes it

Breaking Changes: None
- Existing auto-activation continues unchanged
- Specialist agents unaffected
- User workflows remain the same
- New capability: Automatic documentation and knowledge maintenance

Value Proposition:
Transforms SuperClaude into a continuously learning system that accumulates
knowledge, prevents recurring mistakes, and maintains fresh documentation
without manual intervention.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add Claude Code conversation history management research

Research covering .jsonl file structure, performance impact, and retention policies.

Content:
- Claude Code .jsonl file format and message types
- Performance issues from GitHub (memory leaks, conversation compaction)
- Retention policies (consumer vs enterprise)
- Rotation recommendations based on actual data
- File history snapshot tracking mechanics

Source: Moved from agiletec project (research applicable to all Claude Code projects)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat: add Development documentation structure

Phase 1: Documentation Structure complete

- Add Docs/Development/ directory for development documentation
- Add ARCHITECTURE.md - System architecture with PM Agent meta-layer
- Add ROADMAP.md - 5-phase development plan with checkboxes
- Add TASKS.md - Daily task tracking with progress indicators
- Add PROJECT_STATUS.md - Current status dashboard and metrics
- Add pm-agent-integration.md - Implementation guide for PM Agent mode

This establishes comprehensive documentation foundation for:
- System architecture understanding
- Development planning and tracking
- Implementation guidance
- Progress visibility

Related: #pm-agent-mode #documentation #phase-1

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat: PM Agent session lifecycle and PDCA implementation

Phase 2: PM Agent Mode Integration (Design Phase)

Commands/pm.md updates:
- Add "Always-Active Foundation Layer" concept
- Add Session Lifecycle (Session Start/During Work/Session End)
- Add PDCA Cycle (Plan/Do/Check/Act) automation
- Add Serena MCP Memory Integration (list/read/write_memory)
- Document auto-activation triggers

Agents/pm-agent.md updates:
- Add Session Start Protocol (MANDATORY auto-activation)
- Add During Work PDCA Cycle with example workflows
- Add Session End Protocol with state preservation
- Add PDCA Self-Evaluation Pattern
- Add Documentation Strategy (temp → patterns/mistakes)
- Add Memory Operations Reference

Key Features:
- Session start auto-activation for context restoration
- 30-minute checkpoint saves during work
- Self-evaluation with think_about_* operations
- Systematic documentation lifecycle
- Knowledge evolution to CLAUDE.md

Implementation Status:
-  Design complete (Commands/pm.md, Agents/pm-agent.md)
-  Implementation pending (Core components)
-  Serena MCP integration pending

Salvaged from mistaken development in ~/.claude directory

Related: #pm-agent-mode #session-lifecycle #pdca-cycle #phase-2

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix: disable Serena MCP auto-browser launch

Disable web dashboard and GUI log window auto-launch in Serena MCP server
to prevent intrusive browser popups on startup. Users can still manually
access the dashboard at http://localhost:24282/dashboard/ if needed.

Changes:
- Add CLI flags to Serena run command:
  - --enable-web-dashboard false
  - --enable-gui-log-window false
- Ensures Git-tracked configuration (no reliance on ~/.serena/serena_config.yml)
- Aligns with AIRIS MCP Gateway integration approach

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* refactor: rename directories to lowercase for PEP8 compliance

- Rename superclaude/Agents -> superclaude/agents
- Rename superclaude/Commands -> superclaude/commands
- Rename superclaude/Core -> superclaude/core
- Rename superclaude/Examples -> superclaude/examples
- Rename superclaude/MCP -> superclaude/mcp
- Rename superclaude/Modes -> superclaude/modes

This change follows Python PEP8 naming conventions for package directories.

* style: fix PEP8 violations and update package name to lowercase

Changes:
- Format all Python files with black (43 files reformatted)
- Update package name from 'SuperClaude' to 'superclaude' in pyproject.toml
- Fix import statements to use lowercase package name
- Add missing imports (timedelta, __version__)
- Remove old SuperClaude.egg-info directory

PEP8 violations reduced from 2672 to 701 (mostly E501 line length due to black's 88 char vs flake8's 79 char limit).

* docs: add PM Agent development documentation

Add comprehensive PM Agent development documentation:
- PM Agent ideal workflow (7-phase autonomous cycle)
- Project structure understanding (Git vs installed environment)
- Installation flow understanding (CommandsComponent behavior)
- Task management system (current-tasks.md)

Purpose: Eliminate repeated explanations and enable autonomous PDCA cycles

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat(pm-agent): add self-correcting execution and warning investigation culture

## Changes

### superclaude/commands/pm.md
- Add "Self-Correcting Execution" section with root cause analysis protocol
- Add "Warning/Error Investigation Culture" section enforcing zero-tolerance for dismissal
- Define error detection protocol: STOP → Investigate → Hypothesis → Different Solution → Execute
- Document anti-patterns (retry without understanding) and correct patterns (research-first)

### docs/Development/hypothesis-pm-autonomous-enhancement-2025-10-14.md
- Add PDCA workflow hypothesis document for PM Agent autonomous enhancement

## Rationale

PM Agent must never retry failed operations without understanding root causes.
All warnings and errors require investigation via context7/WebFetch/documentation
to ensure production-quality code and prevent technical debt accumulation.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat(installer): add airis-mcp-gateway MCP server option

## Changes

- Add airis-mcp-gateway to MCP server options in installer
- Configuration: GitHub-based installation via uvx
- Repository: https://github.com/oraios/airis-mcp-gateway
- Purpose: Dynamic MCP Gateway for zero-token baseline and on-demand tool loading

## Implementation

Added to setup/components/mcp.py self.mcp_servers dictionary with:
- install_method: github
- install_command: uvx test installation
- run_command: uvx runtime execution
- required: False (optional server)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: kazuki <kazuki@kazukinoMacBook-Air.local>
Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
kazuki nakai
2025-10-14 12:17:09 +09:00
committed by GitHub
parent 302c5851b1
commit 050d5ea2ab
194 changed files with 9698 additions and 3693 deletions

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# Chrome DevTools MCP Server
**Purpose**: Performance analysis, debugging, and real-time browser inspection
## Triggers
- Performance auditing and analysis requests
- Debugging of layout issues (e.g., CLS)
- Investigation of slow loading times (e.g., LCP)
- Analysis of console errors and network requests
- Real-time inspection of the DOM and CSS
## Choose When
- **For deep performance analysis**: When you need to understand performance bottlenecks.
- **For live debugging**: To inspect the runtime state of a web page and debug live issues.
- **For network analysis**: To inspect network requests and identify issues like CORS errors.
- **Not for E2E testing**: Use Playwright for end-to-end testing scenarios.
- **Not for static analysis**: Use native Claude for code review and logic validation.
## Works Best With
- **Sequential**: Sequential plans a performance improvement strategy → Chrome DevTools analyzes and verifies the improvements.
- **Playwright**: Playwright automates a user flow → Chrome DevTools analyzes the performance of that flow.
## Examples
```
"analyze the performance of this page" → Chrome DevTools (performance analysis)
"why is this page loading slowly?" → Chrome DevTools (performance analysis)
"debug the layout shift on this element" → Chrome DevTools (live debugging)
"check for console errors on the homepage" → Chrome DevTools (live debugging)
"what network requests are failing?" → Chrome DevTools (network analysis)
"test the login flow" → Playwright (browser automation)
"review this function's logic" → Native Claude (static analysis)
```

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# Context7 MCP Server
**Purpose**: Official library documentation lookup and framework pattern guidance
## Triggers
- Import statements: `import`, `require`, `from`, `use`
- Framework keywords: React, Vue, Angular, Next.js, Express, etc.
- Library-specific questions about APIs or best practices
- Need for official documentation patterns vs generic solutions
- Version-specific implementation requirements
## Choose When
- **Over WebSearch**: When you need curated, version-specific documentation
- **Over native knowledge**: When implementation must follow official patterns
- **For frameworks**: React hooks, Vue composition API, Angular services
- **For libraries**: Correct API usage, authentication flows, configuration
- **For compliance**: When adherence to official standards is mandatory
## Works Best With
- **Sequential**: Context7 provides docs → Sequential analyzes implementation strategy
- **Magic**: Context7 supplies patterns → Magic generates framework-compliant components
## Examples
```
"implement React useEffect" → Context7 (official React patterns)
"add authentication with Auth0" → Context7 (official Auth0 docs)
"migrate to Vue 3" → Context7 (official migration guide)
"optimize Next.js performance" → Context7 (official optimization patterns)
"just explain this function" → Native Claude (no external docs needed)
```

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# Magic MCP Server
**Purpose**: Modern UI component generation from 21st.dev patterns with design system integration
## Triggers
- UI component requests: button, form, modal, card, table, nav
- Design system implementation needs
- `/ui` or `/21` commands
- Frontend-specific keywords: responsive, accessible, interactive
- Component enhancement or refinement requests
## Choose When
- **For UI components**: Use Magic, not native HTML/CSS generation
- **Over manual coding**: When you need production-ready, accessible components
- **For design systems**: When consistency with existing patterns matters
- **For modern frameworks**: React, Vue, Angular with current best practices
- **Not for backend**: API logic, database queries, server configuration
## Works Best With
- **Context7**: Magic uses 21st.dev patterns → Context7 provides framework integration
- **Sequential**: Sequential analyzes UI requirements → Magic implements structured components
## Examples
```
"create a login form" → Magic (UI component generation)
"build a responsive navbar" → Magic (UI pattern with accessibility)
"add a data table with sorting" → Magic (complex UI component)
"make this component accessible" → Magic (UI enhancement)
"write a REST API" → Native Claude (backend logic)
"fix database query" → Native Claude (non-UI task)
```

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# Morphllm MCP Server
**Purpose**: Pattern-based code editing engine with token optimization for bulk transformations
## Triggers
- Multi-file edit operations requiring consistent patterns
- Framework updates, style guide enforcement, code cleanup
- Bulk text replacements across multiple files
- Natural language edit instructions with specific scope
- Token optimization needed (efficiency gains 30-50%)
## Choose When
- **Over Serena**: For pattern-based edits, not symbol operations
- **For bulk operations**: Style enforcement, framework updates, text replacements
- **When token efficiency matters**: Fast Apply scenarios with compression needs
- **For simple to moderate complexity**: <10 files, straightforward transformations
- **Not for semantic operations**: Symbol renames, dependency tracking, LSP integration
## Works Best With
- **Serena**: Serena analyzes semantic context → Morphllm executes precise edits
- **Sequential**: Sequential plans edit strategy → Morphllm applies systematic changes
## Examples
```
"update all React class components to hooks" → Morphllm (pattern transformation)
"enforce ESLint rules across project" → Morphllm (style guide application)
"replace all console.log with logger calls" → Morphllm (bulk text replacement)
"rename getUserData function everywhere" → Serena (symbol operation)
"analyze code architecture" → Sequential (complex analysis)
"explain this algorithm" → Native Claude (simple explanation)
```

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# Playwright MCP Server
**Purpose**: Browser automation and E2E testing with real browser interaction
## Triggers
- Browser testing and E2E test scenarios
- Visual testing, screenshot, or UI validation requests
- Form submission and user interaction testing
- Cross-browser compatibility validation
- Performance testing requiring real browser rendering
- Accessibility testing with automated WCAG compliance
## Choose When
- **For real browser interaction**: When you need actual rendering, not just code
- **Over unit tests**: For integration testing, user journeys, visual validation
- **For E2E scenarios**: Login flows, form submissions, multi-page workflows
- **For visual testing**: Screenshot comparisons, responsive design validation
- **Not for code analysis**: Static code review, syntax checking, logic validation
## Works Best With
- **Sequential**: Sequential plans test strategy → Playwright executes browser automation
- **Magic**: Magic creates UI components → Playwright validates accessibility and behavior
## Examples
```
"test the login flow" → Playwright (browser automation)
"check if form validation works" → Playwright (real user interaction)
"take screenshots of responsive design" → Playwright (visual testing)
"validate accessibility compliance" → Playwright (automated WCAG testing)
"review this function's logic" → Native Claude (static analysis)
"explain the authentication code" → Native Claude (code review)
```

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# Sequential MCP Server
**Purpose**: Multi-step reasoning engine for complex analysis and systematic problem solving
## Triggers
- Complex debugging scenarios with multiple layers
- Architectural analysis and system design questions
- `--think`, `--think-hard`, `--ultrathink` flags
- Problems requiring hypothesis testing and validation
- Multi-component failure investigation
- Performance bottleneck identification requiring methodical approach
## Choose When
- **Over native reasoning**: When problems have 3+ interconnected components
- **For systematic analysis**: Root cause analysis, architecture review, security assessment
- **When structure matters**: Problems benefit from decomposition and evidence gathering
- **For cross-domain issues**: Problems spanning frontend, backend, database, infrastructure
- **Not for simple tasks**: Basic explanations, single-file changes, straightforward fixes
## Works Best With
- **Context7**: Sequential coordinates analysis → Context7 provides official patterns
- **Magic**: Sequential analyzes UI logic → Magic implements structured components
- **Playwright**: Sequential identifies testing strategy → Playwright executes validation
## Examples
```
"why is this API slow?" → Sequential (systematic performance analysis)
"design a microservices architecture" → Sequential (structured system design)
"debug this authentication flow" → Sequential (multi-component investigation)
"analyze security vulnerabilities" → Sequential (comprehensive threat modeling)
"explain this function" → Native Claude (simple explanation)
"fix this typo" → Native Claude (straightforward change)
```

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# Serena MCP Server
**Purpose**: Semantic code understanding with project memory and session persistence
## Triggers
- Symbol operations: rename, extract, move functions/classes
- Project-wide code navigation and exploration
- Multi-language projects requiring LSP integration
- Session lifecycle: `/sc:load`, `/sc:save`, project activation
- Memory-driven development workflows
- Large codebase analysis (>50 files, complex architecture)
## Choose When
- **Over Morphllm**: For symbol operations, not pattern-based edits
- **For semantic understanding**: Symbol references, dependency tracking, LSP integration
- **For session persistence**: Project context, memory management, cross-session learning
- **For large projects**: Multi-language codebases requiring architectural understanding
- **Not for simple edits**: Basic text replacements, style enforcement, bulk operations
## Works Best With
- **Morphllm**: Serena analyzes semantic context → Morphllm executes precise edits
- **Sequential**: Serena provides project context → Sequential performs architectural analysis
## Examples
```
"rename getUserData function everywhere" → Serena (symbol operation with dependency tracking)
"find all references to this class" → Serena (semantic search and navigation)
"load my project context" → Serena (/sc:load with project activation)
"save my current work session" → Serena (/sc:save with memory persistence)
"update all console.log to logger" → Morphllm (pattern-based replacement)
"create a login form" → Magic (UI component generation)
```

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# Tavily MCP Server
**Purpose**: Web search and real-time information retrieval for research and current events
## Triggers
- Web search requirements beyond Claude's knowledge cutoff
- Current events, news, and real-time information needs
- Market research and competitive analysis tasks
- Technical documentation not in training data
- Academic research requiring recent publications
- Fact-checking and verification needs
- Deep research investigations requiring multi-source analysis
- `/sc:research` command activation
## Choose When
- **Over WebSearch**: When you need structured search with advanced filtering
- **Over WebFetch**: When you need multi-source search, not single page extraction
- **For research**: Comprehensive investigations requiring multiple sources
- **For current info**: Events, updates, or changes after knowledge cutoff
- **Not for**: Simple questions answerable from training, code generation, local file operations
## Works Best With
- **Sequential**: Tavily provides raw information → Sequential analyzes and synthesizes
- **Playwright**: Tavily discovers URLs → Playwright extracts complex content
- **Context7**: Tavily searches for updates → Context7 provides stable documentation
- **Serena**: Tavily performs searches → Serena stores research sessions
## Configuration
Requires TAVILY_API_KEY environment variable from https://app.tavily.com
## Search Capabilities
- **Web Search**: General web searches with ranking algorithms
- **News Search**: Time-filtered news and current events
- **Academic Search**: Scholarly articles and research papers
- **Domain Filtering**: Include/exclude specific domains
- **Content Extraction**: Full-text extraction from search results
- **Freshness Control**: Prioritize recent content
- **Multi-Round Searching**: Iterative refinement based on gaps
## Examples
```
"latest TypeScript features 2024" → Tavily (current technical information)
"OpenAI GPT updates this week" → Tavily (recent news and updates)
"quantum computing breakthroughs 2024" → Tavily (recent research)
"best practices React Server Components" → Tavily (current best practices)
"explain recursion" → Native Claude (general concept explanation)
"write a Python function" → Native Claude (code generation)
```
## Search Patterns
### Basic Search
```
Query: "search term"
→ Returns: Ranked results with snippets
```
### Domain-Specific Search
```
Query: "search term"
Domains: ["arxiv.org", "github.com"]
→ Returns: Results from specified domains only
```
### Time-Filtered Search
```
Query: "search term"
Recency: "week" | "month" | "year"
→ Returns: Recent results within timeframe
```
### Deep Content Search
```
Query: "search term"
Extract: true
→ Returns: Full content extraction from top results
```
## Quality Optimization
- **Query Refinement**: Iterate searches based on initial results
- **Source Diversity**: Ensure multiple perspectives in results
- **Credibility Filtering**: Prioritize authoritative sources
- **Deduplication**: Remove redundant information across sources
- **Relevance Scoring**: Focus on most pertinent results
## Integration Flows
### Research Flow
```
1. Tavily: Initial broad search
2. Sequential: Analyze and identify gaps
3. Tavily: Targeted follow-up searches
4. Sequential: Synthesize findings
5. Serena: Store research session
```
### Fact-Checking Flow
```
1. Tavily: Search for claim verification
2. Tavily: Find contradicting sources
3. Sequential: Analyze evidence
4. Report: Present balanced findings
```
### Competitive Analysis Flow
```
1. Tavily: Search competitor information
2. Tavily: Search market trends
3. Sequential: Comparative analysis
4. Context7: Technical comparisons
5. Report: Strategic insights
```
### Deep Research Flow (DR Agent)
```
1. Planning: Decompose research question
2. Tavily: Execute planned searches
3. Analysis: Assess URL complexity
4. Routing: Simple → Tavily extract | Complex → Playwright
5. Synthesis: Combine all sources
6. Iteration: Refine based on gaps
```
## Advanced Search Strategies
### Multi-Hop Research
```yaml
Initial_Search:
query: "core topic"
depth: broad
Follow_Up_1:
query: "entities from initial"
depth: targeted
Follow_Up_2:
query: "relationships discovered"
depth: deep
Synthesis:
combine: all_findings
resolve: contradictions
```
### Adaptive Query Generation
```yaml
Simple_Query:
- Direct search terms
- Single concept focus
Complex_Query:
- Multiple search variations
- Boolean operators
- Domain restrictions
- Time filters
Iterative_Query:
- Start broad
- Refine based on results
- Target specific gaps
```
### Source Credibility Assessment
```yaml
High_Credibility:
- Academic institutions
- Government sources
- Established media
- Official documentation
Medium_Credibility:
- Industry publications
- Expert blogs
- Community resources
Low_Credibility:
- User forums
- Social media
- Unverified sources
```
## Performance Considerations
### Search Optimization
- Batch similar searches together
- Cache search results for reuse
- Prioritize high-value sources
- Limit depth based on confidence
### Rate Limiting
- Maximum searches per minute
- Token usage per search
- Result caching duration
- Parallel search limits
### Cost Management
- Monitor API usage
- Set budget limits
- Optimize query efficiency
- Use caching effectively
## Integration with DR Agent Architecture
### Planning Strategy Support
```yaml
Planning_Only:
- Direct query execution
- No refinement needed
Intent_Planning:
- Clarify search intent
- Generate focused queries
Unified:
- Present search plan
- Adjust based on feedback
```
### Multi-Hop Execution
```yaml
Hop_Management:
- Track search genealogy
- Build on previous results
- Detect circular references
- Maintain hop context
```
### Self-Reflection Integration
```yaml
Quality_Check:
- Assess result relevance
- Identify coverage gaps
- Trigger additional searches
- Calculate confidence scores
```
### Case-Based Learning
```yaml
Pattern_Storage:
- Successful query formulations
- Effective search strategies
- Domain preferences
- Time filter patterns
```
## Error Handling
### Common Issues
- API key not configured
- Rate limit exceeded
- Network timeout
- No results found
- Invalid query format
### Fallback Strategies
- Use native WebSearch
- Try alternative queries
- Expand search scope
- Use cached results
- Simplify search terms
## Best Practices
### Query Formulation
1. Start with clear, specific terms
2. Use quotes for exact phrases
3. Include relevant keywords
4. Specify time ranges when needed
5. Use domain filters strategically
### Result Processing
1. Verify source credibility
2. Cross-reference multiple sources
3. Check publication dates
4. Identify potential biases
5. Extract key information
### Integration Workflow
1. Plan search strategy
2. Execute initial searches
3. Analyze results
4. Identify gaps
5. Refine and iterate
6. Synthesize findings
7. Store valuable patterns

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{
"context7": {
"command": "npx",
"args": [
"-y",
"@upstash/context7-mcp@latest"
]
}
}

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{
"magic": {
"type": "stdio",
"command": "npx",
"args": [
"@21st-dev/magic"
],
"env": {
"TWENTYFIRST_API_KEY": ""
}
}
}

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{
"morphllm-fast-apply": {
"command": "npx",
"args": [
"@morph-llm/morph-fast-apply",
"/home/"
],
"env": {
"MORPH_API_KEY": "",
"ALL_TOOLS": "true"
}
}
}

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{
"playwright": {
"command": "npx",
"args": [
"@playwright/mcp@latest"
]
}
}

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@@ -0,0 +1,9 @@
{
"sequential-thinking": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-sequential-thinking"
]
}
}

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{
"serena": {
"command": "docker",
"args": [
"run",
"--rm",
"-v", "${PWD}:/workspace",
"--workdir", "/workspace",
"python:3.11-slim",
"bash", "-c",
"pip install uv && uv tool install serena-ai && uv tool run serena-ai start-mcp-server --context ide-assistant --project /workspace"
]
}
}

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@@ -0,0 +1,13 @@
{
"serena": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/oraios/serena",
"serena",
"start-mcp-server",
"--context",
"ide-assistant"
]
}
}

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@@ -0,0 +1,13 @@
{
"tavily": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://mcp.tavily.com/mcp/?tavilyApiKey=${TAVILY_API_KEY}"
],
"env": {
"TAVILY_API_KEY": "${TAVILY_API_KEY}"
}
}
}