Have Claude Code consult o3/Gemini for complex problems via MCP

This SQL query is timing out on large datasets. Can you help optimize it? Ask Gemini ⏺ I'll examine your query and consult Gemini for database optimization strategies. ⏺ consult-llm:consult_llm (MCP)(prompt: "How can I optimize this slow SQL query for better performance on large datasets?", files: ["queries/user -analytics.sql","schema/tables.sql"], model: "gemini-2.5-pro") ⎿ The query has significant performance bottlenecks: missing indexes on join columns, inefficient subqueries that could be CTEs, and unnecessary DISTINCT operations. Add composite indexes on (user_id, created_at), replace correlated subqueries with window functions, and eliminate redundant GROUP BY clauses. … +287 lines (ctrl+r to expand) ⏺ Great insights! I'll add the suggested indexes, refactor the subqueries to use CTEs with window functions, and optimize the GROUP BY operations.

claude mcp add consult-llm -- npx -y consult-llm-mcp

claude mcp add --scope user consult-llm -- npx -y consult-llm-mcp

claude mcp add \ -e OPENAI_API_KEY=your_openai_key \ -e GEMINI_API_KEY=your_gemini_key \ -e DEEPSEEK_API_KEY=your_deepseek_key \ consult-llm -- npx -y consult-llm-mcp

<Some long explanation of the issue here> Consult LLM for assistance, use gemini ⏺ I see the issue! The problem is that the frontend is trying to use the hint for "isn't" but the server generated a hint for "isn't it" (the full phrase). This is a mismatch between how the frontend segments text and how the server generates hints. Let me consult the LLM to get assistance on this segmentation mismatch issue. ⏺ Write(hint-segmentation-issue.md) ⎿  Wrote 53 lines to hint-segmentation-issue.md # Hint Segmentation Mismatch Issue ## Problem Description There's a mismatch between how the frontend segments text for clickable words and how the backend generates hints. This causes hint unlocking to fail when the segments don't match exactly. ## Example Case **Original Text**: "Hello! Nice weather, isn't it?" … +43 lines (ctrl+r to expand) ⏺ consult-llm:consult_llm (MCP)(model: "gemini-2.5-pro", files: ["/project/hint -segmentation-issue.md","/project/web/src/compo nents/HintsText.tsx","/project/web/src/utils/hi nts.ts","/project/server/src/routes/public/hint s.ts","/project/server/src/llm/hints.ts"]) ⎿ An excellent analysis of the problem. The root cause has been correctly identified: a race condition within the `handleConfirmGenerateHints` function in `HintsText.tsx`. The component state updates in the background after `await generateHints()`, but the function continues executing with a stale `activeSegment` variable, causing it to request a hint for a smaller text segment ( e.g., "isn't") instead of the full, newly-available hint phrase (e.g., "isn't it"). … +607 lines (ctrl+r to expand) ⏺ Perfect! The LLM identified the core issue - there's a race condition in handleConfirmGenerateHints where the function uses stale state after async operations. The solution is to re-target the hint key after generation to find the correct matching hint. Let me implement this solution: ⏺ Update Todos ⎿  ☐ Fix hint segmentation mismatch by implementing smarter hint key targeting in handleConfirmGenerateHints

{ "prompt": "What are the performance implications of using async/await vs Promise.then() in Node.js?" }

{ "prompt": "How can I optimize this authentication middleware?", "files": ["src/auth.ts", "src/middleware.ts"], "model": "o3" }

{ "prompt": "Review these authentication changes for security issues", "files": ["src/auth.ts"], "git_diff": { "files": ["src/auth.ts", "src/middleware.ts"], "base_ref": "main" }, "model": "gemini-2.5-pro" }

[2025-06-22T20:16:04.673Z] TOOL CALL: consult_llm Arguments: { "files": [ "refactor-analysis.md", "src/main.ts", "src/schema.ts", "src/config.ts", "src/llm.ts", "src/llm-cost.ts" ], "model": "deepseek-reasoner" } ================================================================================ [2025-06-22T20:16:04.675Z] PROMPT (model: deepseek-reasoner): ## Relevant Files ### File: src/main.ts ... Please provide specific suggestions for refactoring with example code structure where helpful. ================================================================================ [2025-06-22T20:19:20.632Z] RESPONSE (model: deepseek-reasoner): Based on the analysis, here are the key refactoring suggestions to improve separation of concerns and maintainability: ... This refactoring maintains all existing functionality while significantly improving maintainability and separation of concerns. The new structure makes it easier to add features like new LLM providers, additional context sources, or alternative prompt formats. Tokens: 3440 input, 5880 output | Cost: $0.014769 (input: $0.001892, output: $0.012877)

## consult-llm-mcp Use the `consult_llm` MCP tool to ask a more powerful AI for help with complex problems. Include files to git_diff when asking feedback for changes. Use Gemini 2.5 Pro. CRITICAL: When asking, don't present options, this will bias the answer.