MCP Tool Orchestration Framework

I'll help you set up comprehensive MCP (Model Context Protocol) tool integration for your AI workflows. Let me understand your needs:

## Understanding Your MCP Integration Goals

**User and Context Management:**
- Who is the primary user for this system? (default_user identity)
- What types of information should be remembered? (preferences, goals, relationships, behaviors)
- How should context be maintained across sessions?
- Are there privacy or data retention policies to consider?

**Filesystem Operations:**
- What directories and files will the system access?
- Are there restricted or sensitive directories to avoid?
- What types of file operations are most common? (read, write, edit, search)
- Do you need project structure awareness and code analysis?

**GitHub Integration:**
- Which GitHub repositories will you work with?
- What workflows are most important? (issues, PRs, code review, security scanning)
- Do you need automated commit and branch management?
- Are there team collaboration requirements?

**Web Research Capabilities:**
- What types of information need real-time web access?
- Are there preferred or trusted sources?
- How should web research integrate with local knowledge?
- Do you need citation tracking and source verification?

**Artifact Management:**
- What types of artifacts will be created? (code, documentation, reports, visualizations)
- How should version control be handled?
- Are there output format preferences?
- Do you need artifact sharing or publication workflows?

---

Based on your answers, I'll provide:

## 1. Memory Intelligence Framework

Cognitive memory management:
- **User Identification**: Proactive default_user resolution
- **Context Retrieval**: Session initialization with "Remembering..." preface
- **Knowledge Graph Queries**: Identity, preferences, objectives, relationships
- **Entity Observation**: Continuous monitoring for new information
- **Graph Evolution**: Structured entity, relation, and observation encoding
- **Memory Categories**: Basic identity, behaviors, preferences, goals, relationships

## 2. Filesystem Cognition System

Intelligent file operations:
- **Topological Awareness**: Directory mapping and permission scoping
- **Pre-Modification Validation**: Impact assessment before changes
- **Minimally Invasive Editing**: Atomic diffs vs. full rewrites
- **Project Structure Analysis**: Code organization understanding
- **Search and Discovery**: File location and content search
- **Change Tracking**: Modified file monitoring and history

## 3. Project Comprehension Engine

Codebase analysis capabilities:
- **Structural Context**: Macro and micro project understanding
- **Component Analysis**: Function and class examination
- **Implementation Tracking**: Granular code change monitoring
- **Dependency Mapping**: Import and relationship analysis
- **Code Outline Generation**: High-level structure views
- **Auditability**: Change history and attribution

## 4. GitHub Procedural Framework

Complete GitHub workflow automation:

**Initialization:**
- Session context synchronization (list_notifications, get_me)
- Repository status assessment

**Issue Lifecycle:**
- Issue discovery and filtering (list_issues)
- Detailed issue analysis (get_issue)
- Comment and discussion (add_issue_comment)
- Status updates (update_issue)

**Pull Request Workflow:**
- PR retrieval and file analysis
- Review creation (create_pending_pull_request_review)
- Comment addition (add_review_comment)
- Review submission (submit_pending_pull_request_review)

**Repository Operations:**
- Repository creation and initialization
- Branch management (create_branch)
- File operations (push_files)
- PR creation (create_pull_request)

**Security Operations:**
- Copilot review requests
- Code scanning alert management
- Secret scanning alert handling

## 5. Integrative Web Intelligence

Strategic research integration:
- **Search Strategy**: Query formulation and execution (web_search)
- **Content Retrieval**: Deep content fetching (web_fetch)
- **Semantic Cross-Reference**: Integration with existing knowledge
- **Memory Updates**: Knowledge graph enrichment from research
- **Source Validation**: Credibility and accuracy assessment
- **Application**: Insight integration into current tasks

## 6. Analytical Instrumentation

Computational analysis tools:
- **REPL Environment**: In-browser code execution
- **Data Processing**: Large-scale data analysis (100+ rows)
- **Library Integration**: lodash, papaparse, mathjs
- **File Inspection**: Content analysis within REPL
- **Visualization**: Chart and graph generation
- **Provenance Tracking**: Structured logging and attribution

## 7. Artifact Lifecycle Governance

Comprehensive artifact management:

**Generation Modality:**
- `create`: Novel outputs and new artifacts
- `update`: Incremental changes (<20 lines, <5 locations)
- `rewrite`: Significant refactoring and restructuring

**Quality Practices:**
- No browser storage usage (localStorage/sessionStorage)
- Operational completeness requirements
- Descriptive titling standards
- User feedback reconciliation
- Iterative refinement cycles

## 8. Advanced Orchestration

Multi-tool coordination:
- **Toolchain Orchestration**: Centralized command architecture
- **Memory Fusion**: Persistent, real-time, and inferred data synthesis
- **Cross-Domain Synthesis**: Research + codebase integration
- **Autonomic Fault Recovery**: Intelligent retry frameworks
- **Parallel Execution**: Independent operation coordination

## 9. Session Management Protocol

Structured session lifecycle:

**Bootstrap:**
- User input solicitation
- Cognitive state retrieval
- GitHub context initialization

**Execution Strategy:**
- Project analytics prioritization for engineering
- Research sequencing (search → fetch → inference)
- Edit vs. rewrite decision logic

**Tool Optimization:**
- Memory layer precision
- GitHub file-granular operations
- Filesystem caching strategies
- Artifact classification

## 10. Quality Assurance Framework

System reliability standards:
- **Interactional Coherence**: Repeatable tool invocation patterns
- **Semantic Completeness**: Full context inclusion
- **Change Observability**: State mutation logging
- **Security Posture**: Sanctioned storage only
- **User-Centric Epistemology**: Information fidelity and clarity

## 11. Best Practices Implementation

Operational guidelines:
- Memory updates as discrete graph mutations
- Filesystem actions scoped and verified
- GitHub workflows with canonical logic
- Research integrations validated before memory updates
- Artifact version control and documentation
- Audit trail maintenance

## 12. Success Metrics

Validation criteria:
- User identification and memory anchoring per session
- Structured memory updates with graph mutations
- Verified filesystem operations
- Canonical GitHub workflow compliance
- Validated research integration
- Version-controlled artifact outputs

Tell me about your MCP integration needs and I'll create a comprehensive orchestration framework with memory intelligence, filesystem operations, GitHub automation, and artifact management!