Consult LLM MCP

import { z } from 'zod/v4' export const SupportedChatModel = z.enum([ 'o3', 'gemini-2.5-pro', 'gemini-3-pro-preview', 'deepseek-reasoner', 'gpt-5.1-codex-max', 'gpt-5.1-codex', 'gpt-5.1-codex-mini', 'gpt-5.1', ]) export type SupportedChatModel = z.infer<typeof SupportedChatModel> export const ConsultLlmArgs = z.object({ files: z .array(z.string()) .optional() .describe( 'Array of file paths to include as context. All files are added as context with file paths and code blocks.', ), prompt: z .string() .describe( 'Your question or request for the consultant LLM. Ask neutral, open-ended questions without suggesting specific solutions to avoid biasing the analysis.', ), model: SupportedChatModel.optional() .default('o3') .describe( 'LLM model to use. Prefer gpt-5.1-codex-max when user mentions Codex. This parameter is ignored when `web_mode` is `true`.', ), web_mode: z .boolean() .optional() .default(false) .describe( "If true, copy the formatted prompt to the clipboard instead of querying an LLM. When true, the `model` parameter is ignored. Use this to paste the prompt into browser-based LLM services. IMPORTANT: Only use this when the user specifically requests it. When true, wait for the user to provide the external LLM's response before proceeding with any implementation.", ), git_diff: z .object({ repo_path: z .string() .optional() .describe( 'Path to git repository (defaults to current working directory)', ), files: z .array(z.string()) .min(1, 'At least one file is required for git diff') .describe('Specific files to include in diff'), base_ref: z .string() .optional() .default('HEAD') .describe( 'Git reference to compare against (e.g., "HEAD", "main", commit hash)', ), }) .optional() .describe( 'Generate git diff output to include as context. Shows uncommitted changes by default.', ), }) const consultLlmInputSchema = z.toJSONSchema(ConsultLlmArgs, { target: 'openapi-3.0', }) export const toolSchema = { name: 'consult_llm', description: `Ask a more powerful AI for help with complex problems. Provide your question in the prompt field and always include relevant code files as context. Be specific about what you want: code implementation, code review, bug analysis, architecture advice, etc. IMPORTANT: Ask neutral, open-ended questions. Avoid suggesting specific solutions or alternatives in your prompt as this can bias the analysis. Instead of "Should I use X or Y approach?", ask "What's the best approach for this problem?" Let the consultant LLM provide unbiased recommendations.`, inputSchema: consultLlmInputSchema, } as const