What can you do with this server?

This MCP server bridges Claude 4.5 Sonnet with GLM-4.6's architectural intelligence for expert system design and technical consultation. Core Capabilities: • Architectural Consultation (consult_architecture) - Expert guidance on system design patterns, scalability strategies, distributed systems, microservices, event-driven architectures, and security patterns including threat modeling and zero-trust frameworks • Code Architecture Analysis (analyze_code_architecture) - Evaluate source code for design patterns, SOLID principles, scalability concerns, security implications, and receive refactoring recommendations across multiple languages (TypeScript, Python, Go, Java, etc.) • System Architecture Design (design_system_architecture) - Create complete system architectures from requirements with component breakdowns, data flow modeling, technology stack selection, and deployment strategies • Technical Decision Review (review_technical_decision) - Assess technical decisions with impact analysis, trade-off evaluation, risk assessment, and alternative recommendations Key Features: Horizontal scaling strategies, load balancing, caching hierarchies, service mesh architectures, authentication/authorization frameworks, and real-time consultation integrated into workflows via MCP protocol.

Which integrations are available for this server?

Integrates with Warp Terminal's agent infrastructure to provide real-time architectural consultation capabilities during development workflows

How do I use GLM-4.6 MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GLM-4.6 MCP Server analyze this microservice code for scalability issues" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

GLM-4.6 MCP Server

by bobvasic

Overview Schema Related Servers Score Discussions

TypeScript

Remote

GLM-4.6 Language Models

GLM-4.6 MCP Server

Enterprise Architecture Consultation Protocol

Model Context Protocol bridge enabling Claude 4.5 Sonnet to leverage GLM-4.6's architectural intelligence for advanced system design, scalability patterns, and technical decision-making.

License: MIT Node.js TypeScript

🏗️ System Overview

This MCP server establishes a bi-directional protocol bridge between Claude 4.5 Sonnet and GLM-4.6, enabling real-time architectural consultation during development workflows. The server exposes GLM-4.6's specialized capabilities through standardized MCP tools, facilitating seamless integration with Warp Terminal's agent infrastructure.

Architectural Capabilities

Distributed Systems Design: Microservices patterns, service mesh architectures, event-driven systems
Scalability Engineering: Horizontal scaling strategies, load balancing, caching hierarchies
Security Architecture: Threat modeling, zero-trust patterns, authentication/authorization frameworks
Code Analysis: SOLID principles evaluation, design pattern recognition, refactoring recommendations
Technical Decision Review: Trade-off analysis, risk assessment, alternative approach evaluation
System Architecture Design: Component decomposition, data flow modeling, technology stack selection

Related MCP server: Senior Consult MCP

⚡ Quick Start

Prerequisites

node >= 18.0.0
npm >= 9.0.0
GLM-4.6 API Key from https://open.bigmodel.cn

Installation

cd glm-mcp-server
npm install
npm run build

Environment Configuration

Create .env file in project root:

GLM_API_KEY=your_api_key_here

Security Notice: Never commit .env to version control. Use secure secret management in production environments.

🔧 Warp Terminal Integration

MCP Server Configuration

Add the following configuration to your Warp MCP servers configuration file:

Location: ~/.config/warp-terminal/mcp_servers.json or Warp Settings → MCP Servers

{
  "mcpServers": {
    "glm-architecture": {
      "command": "node",
      "args": ["/absolute/path/to/glm-mcp-server/build/index.js"],
      "env": {
        "GLM_API_KEY": "your_glm_api_key_here"
      }
    }
  }
}

⚠️ Configuration Notes:

Replace /absolute/path/to/glm-mcp-server with your actual installation path
Replace your_glm_api_key_here with your actual GLM API key
Restart Warp Terminal after configuration changes

Verification

# Test server functionality
node build/index.js
# Expected output: "GLM-4.6 MCP Server running on stdio"

📡 MCP Tools Reference

1. `consult_architecture`

General architectural consultation for system design patterns, scalability strategies, and technical guidance.

Input Schema:

{
  query: string;        // Architectural question requiring expert consultation
  context?: string;     // Optional system context, requirements, constraints
}

Use Case: High-level architectural decisions, pattern selection, scalability planning

2. `analyze_code_architecture`

Architectural analysis of source code including design patterns, SOLID principles, and improvement recommendations.

Input Schema:

{
  code: string;         // Source code to analyze
  language: string;     // Programming language (typescript, python, go, java, etc.)
  question: string;     // Specific architectural question about the code
}

Use Case: Code review, refactoring planning, design pattern evaluation

3. `design_system_architecture`

Complete system architecture design from requirements including component breakdown, data flow, and deployment strategies.

Input Schema:

{
  requirements: string; // Detailed system requirements, constraints, objectives
}

Use Case: New system design, architecture documentation, technology selection

4. `review_technical_decision`

Technical decision review with impact assessment, trade-off analysis, and alternative recommendations.

Input Schema:

{
  decision: string;     // Technical decision to review
  context: string;      // Current architecture, constraints, objectives
}

Use Case: Architecture review, technology evaluation, risk assessment

🔬 Usage Examples

Example 1: Architectural Consultation

Within Warp Terminal, Claude can invoke:

// Claude automatically calls via MCP
consult_architecture({
  query: "What's the optimal caching strategy for a high-traffic API with 10k req/s?",
  context: "Node.js microservices, PostgreSQL database, AWS infrastructure"
})

Example 2: Code Architecture Analysis

analyze_code_architecture({
  code: `class UserService { ... }`,
  language: "typescript",
  question: "Does this service follow clean architecture principles?"
})

Example 3: System Design

design_system_architecture({
  requirements: `
    - Real-time messaging platform
    - 1M concurrent users
    - Sub-100ms latency
    - 99.99% uptime SLA
    - Global distribution
  `
})

🏛️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                      Warp Terminal                          │
│  ┌──────────────────────────────────────────────────────┐   │
│  │              Claude 4.5 Sonnet Agent                 │   │
│  └────────────────────┬─────────────────────────────────┘   │
└───────────────────────┼─────────────────────────────────────┘
                        │ MCP Protocol (stdio)
                        ▼
┌─────────────────────────────────────────────────────────────┐
│                  GLM MCP Server (Node.js)                   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  MCP Protocol Handler  │  Tool Registry              │   │
│  ├──────────────────────────────────────────────────────┤   │
│  │            GLM-4.6 API Client Layer                  │   │
│  │  • Authentication  • Error Handling  • Retry Logic   │   │
│  └──────────────────────────────────────────────────────┘   │
└────────────────────────┬────────────────────────────────────┘
                         │ HTTPS/REST
                         ▼
┌─────────────────────────────────────────────────────────────┐
│          GLM-4.6 API (open.bigmodel.cn)                     │
│              Zhipu AI Model Inference                       │
└─────────────────────────────────────────────────────────────┘

🛠️ Development

Build

npm run build      # Compile TypeScript to JavaScript
npm run watch      # Development mode with auto-rebuild

Project Structure

glm-mcp-server/
├── src/
│   ├── index.ts           # MCP server entry point
│   └── glm-client.ts      # GLM-4.6 API client
├── build/                 # Compiled JavaScript output
├── package.json           # Dependencies and scripts
├── tsconfig.json          # TypeScript configuration
└── .env                   # Environment variables (not in VCS)

🔐 Security Considerations

API Key Management: Store GLM_API_KEY in environment variables, never in code
Transport Security: All API communications use HTTPS/TLS
Input Validation: All tool inputs are validated before processing
Error Handling: Sensitive information is sanitized from error messages
Rate Limiting: Implement client-side rate limiting for production deployments

📊 Performance Characteristics

Metric	Specification
Latency	2-8s (model inference dependent)
Throughput	API key tier dependent
Timeout	60s default (configurable)
Max Token Output	4096 tokens
Concurrent Requests	Single instance: 1 (sequential processing)

🐛 Troubleshooting

Server Not Starting

# Verify Node.js version
node --version  # Must be >= 18.0.0

# Check build output
npm run build

# Verify GLM_API_KEY is set
echo $GLM_API_KEY

API Authentication Errors

Verify API key validity at https://open.bigmodel.cn
Check API key has sufficient quota
Ensure no whitespace in .env file

Warp Terminal Integration Issues

Restart Warp Terminal after configuration changes
Verify absolute path in MCP configuration
Check Warp logs: Warp → Settings → Advanced → View Logs

📚 Resources

GLM-4.6 Documentation: https://docs.z.ai/guides/llm/glm-4.6
Model Context Protocol: https://modelcontextprotocol.io
Warp MCP Integration: https://docs.warp.dev/features/agent-mode/model-context-protocol

📝 License

🤝 Support

Enterprise Support: info@cyberlinksec.com

Issue Reporting: Include server logs, Warp version, and reproduction steps

Built with Enterprise Standards by CyberLink Security & Raptor Labs

Empowering AI-Driven Architecture Decision Intelligence

Available Tools

5 tools

advanced_reasoningA

Consult GLM-4.6 for advanced mathematical, algorithmic, and scientific reasoning tasks. Delivers world-class innovative solutions with rigorous methodology, optimal algorithms, and enterprise-grade quality. Use for: complex algorithms, mathematical proofs, performance optimization, advanced data structures, computational problems, scientific analysis. Response optimized for Claude 4.5 Sonnet with XML structure.

ParametersJSON Schema

Name	Required	Description
`task`	Yes	The specific mathematical, algorithmic, or scientific task requiring advanced reasoning
`context`	Yes	Comprehensive context including: problem domain, constraints, requirements, current approach (if any), performance requirements, business logic
`expected_outcome`	Yes	Detailed description of expected outcome: solution characteristics, performance targets, quality metrics, innovation requirements

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that the tool consults GLM-4.6, delivers rigorous methodology, and outputs XML structure optimized for Claude 4.5 Sonnet. This goes beyond basic purpose and gives useful behavioral insight.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, consisting of two sentences that front-load the core purpose and key use cases. Every phrase adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given three required parameters and no output schema, the description covers the tool's purpose, use cases, and even hints at the output format. It is mostly complete but could elaborate on limitations or error handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% parameter description coverage, so the schema already documents each parameter adequately. The description does not add new parameter-level detail beyond the schema, meeting the baseline expectation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool for advanced mathematical, algorithmic, and scientific reasoning tasks. It lists specific use cases such as complex algorithms, mathematical proofs, and performance optimization. This effectively differentiates it from sibling tools focused on architecture and code analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly lists when to use the tool by enumerating domains and tasks. However, it does not provide explicit guidance on when not to use it or mention alternative tools, though the sibling context implies boundaries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

analyze_code_architectureA

Analyze code from an architectural perspective using GLM-4.6. Evaluates design patterns, SOLID principles, scalability, security implications, and provides improvement recommendations.

ParametersJSON Schema

Name	Required	Description
`code`	Yes	The source code to analyze
`language`	Yes	Programming language of the code (e.g., typescript, python, go, java)
`question`	Yes	Specific architectural question about the code

TDQS

A3.8/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the tool uses GLM-4.6 and evaluates specific aspects, but does not explicitly state whether it is read-only, what permissions are needed, or any potential side effects. The description is adequate but lacks depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently communicates purpose and scope without unnecessary words. Every part adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three required parameters and no output schema or annotations, the description covers the key aspects of what is analyzed. However, it lacks information about the output format or structure, which would enhance completeness. Overall, it is nearly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (all parameters have descriptions in schema). The description does not add additional meaning beyond the schema—it focuses on overall analysis scope rather than parameter-specific details. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it analyzes code from an architectural perspective using a specific model (GLM-4.6) and lists evaluation areas (design patterns, SOLID, scalability, security, improvements). This distinguishes it from siblings like 'consult_architecture' or 'design_system_architecture'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for architectural analysis of code but provides no explicit guidance on when to use it versus siblings, nor does it mention prerequisites or exclusions. Usage is inferred but not clarified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

consult_architectureA

Consult GLM-4.6 for expert software architecture guidance, system design patterns, scalability strategies, and technical decision-making. Use this for high-level architectural questions requiring deep technical expertise.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes	The architectural question or problem requiring expert consultation
`context`	No	Optional additional context about the system, requirements, or constraints

TDQS

A3.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It lacks details about behavioral traits such as response format, limitations, or interaction style beyond generic 'guidance'. This leaves the agent unclear on what to expect.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences front-loaded with purpose and usage. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple consultation tool with no output schema or annotations, the description covers purpose and usage but omits behavioral details and return format. Adequate but not complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The tool description does not add any additional meaning beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies 'Consult GLM-4.6 for expert software architecture guidance...' with clear verb and resource, and distinguishes from siblings by focusing on high-level architectural questions requiring deep expertise.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use: 'Use this for high-level architectural questions requiring deep technical expertise.' However, it does not mention when not to use or directly contrast with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

design_system_architectureB

Design a complete system architecture based on requirements using GLM-4.6. Provides component breakdown, data flow patterns, technology recommendations, and deployment strategies.

ParametersJSON Schema

Name	Required	Description	Default
`requirements`	Yes	Detailed system requirements, constraints, and objectives

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. It states the tool 'designs' but does not mention side effects (e.g., whether it creates any permanent state), authorization needs, rate limits, or response characteristics. The mention of 'GLM-4.6' is a technical note, not a behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that front-loads the action ('Design a complete system architecture') and then enumerates outputs. It is efficient with no wasted words. However, it could be restructured to improve readability (e.g., list outputs separately).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (architecture design) and lack of output schema, the description is somewhat sufficient by listing expected outputs. However, it omits any indication of output format, depth, or constraints (e.g., size limits). With no annotations and no output schema, additional context would help agents understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter 'requirements', which is described as 'Detailed system requirements, constraints, and objectives'. The tool description adds minimal extra meaning beyond noting it is 'based on requirements'. Baseline 3 is appropriate as the schema already carries the parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: designing system architecture. It lists specific deliverables (component breakdown, data flow patterns, technology recommendations, deployment strategies), making the purpose concrete. While it doesn't explicitly differentiate from siblings, the verb 'design' and outputs distinguish it from analysis, consultation, or review tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use when requirements are available ('based on requirements'), but it provides no explicit guidance on when to use this tool versus siblings, nor any exclusion criteria. Without context on prerequisites or alternatives, agents must infer appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

review_technical_decisionA

Review and evaluate a technical decision using GLM-4.6 architectural expertise. Assesses impact, trade-offs, alternatives, risks, and provides recommendations.

ParametersJSON Schema

Name	Required	Description	Default
`decision`	Yes	The technical decision to review
`context`	Yes	Context including current architecture, constraints, and objectives

TDQS

A3.7/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It describes the tool's function (assessing impact, etc.) but does not disclose whether it is read-only, has side effects, or any limitations. It is adequate but could be improved with more behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no wasted words. The first sentence states the core purpose, the second expands on the evaluation scope. Excellent front-loading and efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the main purpose and evaluation areas but does not specify the return format or any error cases. With no output schema, some description of the output structure would improve completeness. Adequate for a simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with clear parameter descriptions. The tool description adds context about the expertise used but does not provide substantial additional meaning beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Review and evaluate' and the resource 'technical decision'. It adds specificity by mentioning 'using GLM-4.6 architectural expertise' and lists what will be assessed (impact, trade-offs, alternatives, risks) and output (recommendations). This distinguishes it from sibling tools like 'consult_architecture' which are more general.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like 'analyze_code_architecture' or 'design_system_architecture'. The description implies usage for structured decision review but does not state when not to use it or provide context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

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TDQS

A3.8/5.0

Disambiguation4/5

Tools are generally distinct: advanced_reasoning covers general complex reasoning, while others focus on architecture tasks. However, consult_architecture and analyze_code_architecture could be confused as both involve architecture guidance, though descriptions differentiate code-level vs. high-level.

Naming Consistency4/5

Four tools follow a verb_noun pattern (analyze_code_architecture, consult_architecture, design_system_architecture, review_technical_decision). advanced_reasoning breaks this pattern as adjective_noun, causing minor inconsistency.

Tool Count5/5

5 tools is well-scoped for a server focused on reasoning and architecture tasks. Each tool has a clear purpose and the count is neither too small nor too large.

Completeness4/5

The tool set covers major architecture tasks (analysis, consulting, design, review) and general reasoning. No obvious gaps given the domain, though additional tools for specific areas like code generation could be considered a minor gap.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

Reddit Discussion

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GLM-4.6 MCP Server

Enterprise Architecture Consultation Protocol

🏗️ System Overview

Architectural Capabilities

⚡ Quick Start

Prerequisites

Installation

Environment Configuration

🔧 Warp Terminal Integration

MCP Server Configuration

Verification

📡 MCP Tools Reference

1. consult_architecture

2. analyze_code_architecture

3. design_system_architecture

4. review_technical_decision

🔬 Usage Examples

Example 1: Architectural Consultation

Example 2: Code Architecture Analysis

Example 3: System Design

🏛️ Architecture

🛠️ Development

Build

Project Structure

🔐 Security Considerations

📊 Performance Characteristics

🐛 Troubleshooting

Server Not Starting

API Authentication Errors

Warp Terminal Integration Issues

📚 Resources

📝 License

🤝 Support

Built with Enterprise Standards by CyberLink Security & Raptor Labs

Available Tools

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

1. `consult_architecture`

2. `analyze_code_architecture`

3. `design_system_architecture`

4. `review_technical_decision`