consult_architecture

Instructions

Implementation Reference

• src/index.ts:142-153 (handler)

  Handler that extracts 'query' and 'context' arguments and delegates to glmClient.consultArchitecture().

  case 'consult_architecture': {
    const { query, context } = args as { query: string; context?: string };
    const response = await glmClient.consultArchitecture(query, context);
    return {
      content: [
        {
          type: 'text',
          text: response,
        },
      ],
    };
  }

• src/index.ts:26-41 (schema)

  Tool definition with inputSchema requiring 'query' (string) and optional 'context' (string).

  name: 'consult_architecture',
  description: '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.',
  inputSchema: {
    type: 'object',
    properties: {
      query: {
        type: 'string',
        description: 'The architectural question or problem requiring expert consultation',
      },
      context: {
        type: 'string',
        description: 'Optional additional context about the system, requirements, or constraints',
      },
    },
    required: ['query'],
  },

• src/index.ts:24-42 (registration)

  Tool is registered in the 'tools' array (line 24) and listed via ListToolsRequestSchema handler at line 133.

  const tools: Tool[] = [
    {
      name: 'consult_architecture',
      description: '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.',
      inputSchema: {
        type: 'object',
        properties: {
          query: {
            type: 'string',
            description: 'The architectural question or problem requiring expert consultation',
          },
          context: {
            type: 'string',
            description: 'Optional additional context about the system, requirements, or constraints',
          },
        },
        required: ['query'],
      },
    },

• src/glm-client.ts:68-122 (helper)

  Core helper method in GLMClient that builds the system prompt, constructs the GLM messages, calls the GLM-4.6 chat completions API, and returns the response content.

    async consultArchitecture(query: string, context?: string): Promise<string> {
      const systemPrompt = `You are an elite software architecture consultant specializing in enterprise-grade system design, scalability patterns, security architecture, and technical decision-making. 
  
  Your expertise includes:
  - Distributed systems architecture and microservices design
  - Cloud-native patterns and containerization strategies
  - Database architecture and data modeling
  - API design (REST, GraphQL, gRPC)
  - Security architecture and threat modeling
  - Performance optimization and scalability
  - DevOps and CI/CD pipeline architecture
  - Modern frontend and backend frameworks
  - System integration patterns
  
  Provide concise, actionable architectural guidance with enterprise-grade best practices. Focus on technical accuracy, scalability, maintainability, and security.`;
  
      const messages: GLMMessage[] = [
        { role: 'system', content: systemPrompt },
      ];
  
      if (context) {
        messages.push({
          role: 'user',
          content: `Context:\n${context}\n\nArchitectural Query:\n${query}`,
        });
      } else {
        messages.push({ role: 'user', content: query });
      }
  
      const request: GLMRequest = {
        model: this.model,
        messages,
        temperature: 0.7,
        top_p: 0.9,
        max_tokens: 4096,
        stream: false,
      };
  
      try {
        const response = await this.client.post<GLMResponse>('/chat/completions', request);
        
        if (!response.data.choices || response.data.choices.length === 0) {
          throw new Error('GLM-4.6 returned empty response');
        }
  
        return response.data.choices[0].message.content;
      } catch (error) {
        if (axios.isAxiosError(error)) {
          const status = error.response?.status;
          const message = error.response?.data?.error?.message || error.message;
          throw new Error(`GLM-4.6 API Error (${status}): ${message}`);
        }
        throw error;
      }
    }