mcpctl/.taskmaster/tasks/task_029.md

Task ID: 29

Title: Implement LLM pre-processing pipeline in mcplocal

Status: done

Dependencies: 25 ✓, 27 ✓, 28 ✓

Priority: high

Description: Create the core LLM pre-processing pipeline that intercepts MCP tool calls, uses a local LLM to optimize requests before sending to mcpd, and filters responses before returning to Claude.

Details:

1. Create src/mcplocal/src/llm/processor.ts - the core pipeline:

   export class LlmProcessor {
     constructor(
       private providerRegistry: ProviderRegistry,
       private config: LlmProcessorConfig
     ) {}
   
     async preprocessRequest(toolName: string, params: unknown): Promise<ProcessedRequest> {
       // Use LLM to interpret and optimize the request
       const prompt = this.buildRequestPrompt(toolName, params);
       const result = await this.providerRegistry.getActiveProvider().complete({
         systemPrompt: REQUEST_OPTIMIZATION_SYSTEM_PROMPT,
         userPrompt: prompt
       });
       return this.parseOptimizedRequest(result);
     }
   
     async filterResponse(toolName: string, originalRequest: unknown, rawResponse: unknown): Promise<FilteredResponse> {
       // Use LLM to filter/summarize the response
       const prompt = this.buildFilterPrompt(toolName, originalRequest, rawResponse);
       const result = await this.providerRegistry.getActiveProvider().complete({
         systemPrompt: RESPONSE_FILTER_SYSTEM_PROMPT,
         userPrompt: prompt
       });
       return this.parseFilteredResponse(result);
     }
   }
   

2. Create system prompts in src/mcplocal/src/llm/prompts.ts:
   • REQUEST_OPTIMIZATION_SYSTEM_PROMPT: instruct LLM to generate optimal queries
   • RESPONSE_FILTER_SYSTEM_PROMPT: instruct LLM to extract relevant information
3. Integrate into router.ts - wrap tools/call handler:

   async handleToolsCall(request: JsonRpcRequest) {
     if (this.shouldPreprocess(request.params.name)) {
       const processed = await this.llmProcessor.preprocessRequest(...);
       // Call mcpd with processed request
       const rawResponse = await this.callMcpd(processed);
       const filtered = await this.llmProcessor.filterResponse(...);
       return filtered;
     }
     return this.callMcpd(request.params);
   }
   

4. Add configuration options:
   • enablePreprocessing: boolean
   • preprocessingExclude: string[] (tool names to skip)
   • preferredProvider: string (ollama, gemini, deepseek, etc.)
5. Add bypass logic for simple operations (list, create, delete)

Test Strategy:

1. Unit test: Request preprocessing generates optimized queries
2. Unit test: Response filtering reduces data volume
3. Unit test: Bypass logic works for excluded tools
4. Integration test: Full pipeline with mock LLM provider
5. Test error handling: LLM failure falls back to unfiltered pass-through
6. Test configuration options are respected
7. Measure: response size reduction percentage