GLM-4.6 MCP Server

# GLM-4.6 Consultation Workflow for Claude 4.5 Sonnet ## Overview This document defines the enhanced workflow logic for when Claude 4.5 Sonnet should consult GLM-4.6 for premium-quality, innovative solutions. --- ## Automatic Consultation Triggers GLM-4.6 will **always** be consulted for: ### 1. **Advanced Mathematical Analysis** - Complex mathematical proofs - Abstract mathematical research - Computational mathematics - Optimization problems - Statistical modeling - Numerical analysis - Mathematical algorithm design ### 2. **Algorithm Development** - Custom tailored innovative algorithms - Performance-critical algorithm design - Advanced data structures - Computational complexity analysis - Algorithm optimization - Novel algorithmic approaches - Distributed algorithm design ### 3. **Scientific Computing** - Scientific research methodologies - Data-driven analysis - Computational simulations - Research-grade solutions - Quantitative reasoning - Scientific problem-solving ### 4. **Enterprise-Grade Code Optimization** - Maximum performance code - Industry-grade optimization - Scalability engineering - High-performance computing - Production-grade refactoring - Performance bottleneck resolution ### 5. **Innovative Solution Design** - Breakthrough technical approaches - Novel problem-solving strategies - Groundbreaking system architectures - Cutting-edge implementations - Research-to-production transitions --- ## Consultation Protocol ### Phase 1: Problem Analysis (Claude) Claude receives the user's request and: 1. Identifies if task requires GLM-4.6 consultation 2. Extracts key requirements and constraints 3. Prepares comprehensive context package ### Phase 2: GLM Consultation (via MCP) Claude invokes `advanced_reasoning` MCP tool with: ```typescript { task: "Specific technical challenge requiring world-class solution", context: ` <problem_domain>Domain and technical context</problem_domain> <constraints>Technical and business constraints</constraints> <requirements>Functional and non-functional requirements</requirements> <current_approach>Existing approach if any</current_approach> <performance_requirements>Performance targets and metrics</performance_requirements> <business_logic>Business objectives and expected value</business_logic> `, expected_outcome: ` <solution_characteristics>What makes an ideal solution</solution_characteristics> <performance_targets>Quantitative performance goals</performance_targets> <quality_metrics>Success criteria and benchmarks</quality_metrics> <innovation_requirements>Level of innovation expected</innovation_requirements> ` } ``` ### Phase 3: GLM Processing GLM-4.6 receives enhanced prompt with: - **Task Specification**: Clear mission statement - **Problem Context**: Comprehensive problem background - **Execution Requirements**: Mathematical rigor, algorithmic excellence, innovation, quality - **Response Structure**: XML-tagged sections optimized for Claude - **Quality Standards**: Top 1% industry solutions, research-grade rigor ### Phase 4: Response Formatting GLM-4.6 returns structured XML response: ```xml <glm_response type="advanced_reasoning"> <analysis> - Problem decomposition - Mathematical formulation - Complexity analysis - Constraint identification </analysis> <solution_design> - Core algorithm/approach - Innovation highlights - Optimization strategies - Scalability considerations </solution_design> <implementation_blueprint> - Pseudocode with annotations - Key implementation patterns - Performance characteristics - Edge case handling </implementation_blueprint> <validation_strategy> - Correctness proofs - Test scenarios - Benchmark expectations - Quality metrics </validation_strategy> <production_guidance> - Integration recommendations - Monitoring strategies - Maintenance considerations - Documentation requirements </production_guidance> </glm_response> ``` ### Phase 5: Implementation (Claude) Claude receives GLM's structured response and: 1. Parses XML-structured guidance 2. Implements the solution with enterprise standards 3. Applies additional optimizations 4. Ensures code quality and documentation 5. Validates against requirements --- ## Response Optimization for Claude All GLM-4.6 responses follow **Anthropic Claude 4 Best Practices**: ### XML Structure Usage - Clear tag hierarchy for parsing - Semantic tag names - Nested structure for complex content - Consistent formatting ### Clarity & Precision - Direct, actionable guidance - No ambiguous language - Specific implementation steps - Quantitative metrics where possible ### Example-Driven - Concrete pseudocode - Implementation patterns - Edge case demonstrations - Performance benchmarks ### Production-Ready - Enterprise-grade quality - Security considerations - Scalability built-in - Maintenance guidance --- ## Quality Standards Every GLM-4.6 consultation delivers: ✅ **Mathematical Rigor**: Formal proofs and complexity analysis ✅ **Algorithmic Excellence**: Optimal time/space complexity ✅ **Innovation**: Breakthrough approaches beyond conventional ✅ **Enterprise Quality**: Production-ready implementations ✅ **Performance**: Maximum efficiency and scalability ✅ **Scientific Method**: Data-driven, research-backed solutions --- ## Example Use Cases ### Use Case 1: Algorithm Optimization ``` User Request: "Optimize this sorting algorithm for 100M records" Claude → GLM via advanced_reasoning: - Task: Design optimal sorting approach for massive dataset - Context: Current O(n log n) approach, memory constraints, distributed system - Expected: Sub-linear improvements, parallel processing, production-grade GLM Response: - Mathematical analysis of complexity - Hybrid radix-quicksort algorithm - Cache-aware optimizations - Distributed processing strategy - Benchmark projections Claude → Implementation: - Writes optimized code - Adds monitoring - Creates comprehensive tests ``` ### Use Case 2: Novel Data Structure ``` User Request: "Create a data structure for real-time geospatial queries" Claude → GLM via advanced_reasoning: - Task: Design innovative geospatial index - Context: Billions of points, sub-100ms query latency, write-heavy workload - Expected: Novel approach, better than R-tree, scalable GLM Response: - Hybrid quad-tree + bloom filter design - Mathematical proof of query bounds - Memory optimization strategies - Concurrent access patterns Claude → Implementation: - Implements data structure - Performance tests - Production integration guide ``` ### Use Case 3: Scientific Algorithm ``` User Request: "Implement Monte Carlo simulation for financial modeling" Claude → GLM via advanced_reasoning: - Task: High-accuracy Monte Carlo with variance reduction - Context: Options pricing, millions of paths, accuracy requirements - Expected: Research-grade quality, GPU-accelerated GLM Response: - Advanced variance reduction techniques - Quasi-random number integration - GPU parallelization strategy - Statistical validation methods Claude → Implementation: - Production code with CUDA - Validation suite - Performance benchmarks ``` --- ## Integration with Existing Tools | Tool | Primary Use | When to Use GLM Instead | |------|-------------|------------------------| | `consult_architecture` | General architecture | When mathematical modeling required | | `analyze_code_architecture` | Code review | When algorithmic optimization needed | | `design_system_architecture` | System design | When novel algorithms are core | | `review_technical_decision` | Decision analysis | When computational proof needed | | **`advanced_reasoning`** | **Math/Algo/Science** | **Always for complex reasoning** | --- ## Prompt Engineering Guidelines When Claude invokes GLM-4.6, prompts must include: ### Essential Context - Problem domain and background - Technical constraints (performance, memory, latency) - Business requirements and objectives - Current approach and limitations - Success criteria and metrics ### Quality Expectations - Innovation level required (incremental vs breakthrough) - Performance targets (quantitative) - Quality standards (research-grade, production-grade, prototype) - Timeline and resource constraints ### Structured Format - Use XML tags for clarity - Separate concerns logically - Provide examples when applicable - State assumptions explicitly --- ## Performance Characteristics | Metric | Standard Consult | Advanced Reasoning | |--------|------------------|-------------------| | Timeout | 60s | 120s | | Max Tokens | 4096 | 8192 | | Temperature | 0.7 | 0.8 (more innovative) | | Top P | 0.9 | 0.95 | | Response Format | Text | XML-structured | --- ## Monitoring & Quality Assurance Track these metrics: - GLM consultation frequency - Response quality (Claude's assessment) - Implementation success rate - Performance vs. expectations - Innovation level achieved --- ## Best Practices ### For Claude (when consulting GLM): 1. ✅ Provide comprehensive context 2. ✅ State explicit expectations 3. ✅ Request structured XML responses 4. ✅ Validate GLM's mathematical reasoning 5. ✅ Enhance with additional context if needed ### For GLM (response requirements): 1. ✅ Use XML tags consistently 2. ✅ Include mathematical proofs 3. ✅ Provide complexity analysis 4. ✅ Suggest implementation patterns 5. ✅ Include validation strategies --- ## Escalation Path If standard `advanced_reasoning` insufficient: 1. **Refine Context**: Add more domain knowledge 2. **Iterate**: Re-consult with GLM feedback 3. **Multi-Stage**: Break into sub-problems 4. **Hybrid Approach**: Combine GLM math + Claude implementation 5. **External Research**: Recommend academic papers or domain experts --- ## Version History - **v1.0.0** - Initial workflow with advanced_reasoning tool - Enhanced prompting with Anthropic best practices - XML-structured responses for Claude parsing - Extended token limits for comprehensive analysis --- **Maintained by**: CyberLink Security **Contact**: info@cyberlinksec.com **Last Updated**: 2025-01-18