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server for consulting powerful AI models","maintainers":[{"name":"rane","email":"raine.virta@gmail.com"}],"readme":"# consult-llm-mcp\n\nAn MCP server that lets Claude Code consult stronger AI models (GPT-5.4, Gemini\n3.1 Pro, Claude Opus 4.7, DeepSeek Reasoner, MiniMax M2.7) when Sonnet has you running in circles and you need\nto bring in the heavy artillery. Supports multi-turn conversations.\n\n```\n> Still getting this error after your fix. Ask gemini\n  E5108: Error executing lua: attempt to call method 'child' (a nil value)\n\n⏺ Let me consult Gemini to understand the Neovim treesitter API changes:\n\n⏺ consult-llm:consult_llm (MCP)(prompt: \"Neovim plugin broken due to treesitter\n                                API changes...\", files: [\"lua/testonly.lua\"],\n                                model: \"gemini-2.5-pro\")\n  ⎿ The issue is that iter_matches() can return nil nodes in newer Neovim\n    versions. Switch to iter_captures() which never returns nil nodes, and use\n    named_child(0) instead of child(0) to get only meaningful syntax nodes.\n    … +93 lines (ctrl+r to expand)\n\n⏺ The LLM identified the issue - iter_matches() API changed. Switching to\n  iter_captures() approach now.\n```\n\n[Quick start](#quick-start) · [Configuration](#configuration) ·\n[Skills](#skills) · [Monitor TUI](#monitor) · [Why MCP?](#why-mcp-and-not-cli) ·\n[Changelog](CHANGELOG.md)\n\n## Features\n\n- Query powerful AI models (GPT-5.4, Gemini 3.1 Pro, Claude Opus 4.7, DeepSeek\n  Reasoner, MiniMax M2.7) with\n  relevant files as context\n- Include git changes for code review\n- Comprehensive logging with cost estimation (if using API)\n- [Monitor TUI](#monitor): Real-time dashboard for watching active consultations\n- [Gemini CLI backend](#gemini-cli): Use the `gemini` CLI for Gemini models\n- [Codex CLI backend](#codex-cli): Use the `codex` CLI for OpenAI models\n- [Cursor CLI backend](#cursor-cli): Use the `cursor-agent` CLI to route GPT and\n  Gemini models through a single tool\n- [OpenCode CLI backend](#opencode-cli): Use `opencode` CLI with Copilot, OpenRouter,\n  or any of 75+ providers\n- [Multi-turn conversations](#multi-turn-conversations): Resume CLI sessions\n  across requests with `thread_id`\n- [Web mode](#web-mode): Copy formatted prompts to clipboard for browser-based\n  LLM services\n- [Skills](#skills): Multi-LLM debate, collaboration, and consultation workflows\n- Less is more: Single MCP tool to not clutter the context\n\n<img src=\"meta/monitor-screenshot.webp\" alt=\"consult-llm-monitor screenshot\" width=\"600\">\n\n## Quick start\n\n1. **Add to Claude Code** (choose one):\n\n   **With npx** (no install required):\n\n   ```bash\n   claude mcp add consult-llm \\\n     -e CONSULT_LLM_GEMINI_BACKEND=gemini-cli \\\n     -e CONSULT_LLM_OPENAI_BACKEND=codex-cli \\\n     -- npx -y consult-llm-mcp\n   ```\n\n   This is the recommended setup. Uses [Gemini CLI](#gemini-cli) and\n   [Codex CLI](#codex-cli). No API keys required, just `gemini login` and\n   `codex login`.\n\n   **With binary** (comes with the monitor TUI, no Node.js required):\n\n   ```bash\n   curl -fsSL https://raw.githubusercontent.com/raine/consult-llm-mcp/main/scripts/install.sh | bash\n   ```\n\n   ```bash\n   claude mcp add consult-llm \\\n     -e CONSULT_LLM_GEMINI_BACKEND=gemini-cli \\\n     -e CONSULT_LLM_OPENAI_BACKEND=codex-cli \\\n     -- consult-llm-mcp\n   ```\n\n   For global availability across projects, add `--scope user`.\n\n   **Using API keys instead of CLI backends:**\n\n   ```bash\n   claude mcp add consult-llm \\\n     -e OPENAI_API_KEY=your_openai_key \\\n     -e GEMINI_API_KEY=your_gemini_key \\\n     -e ANTHROPIC_API_KEY=your_anthropic_key \\\n     -e DEEPSEEK_API_KEY=your_deepseek_key \\\n     -e MINIMAX_API_KEY=your_minimax_key \\\n     -- npx -y consult-llm-mcp\n   ```\n\n2. **Verify connection** with `/mcp`:\n\n   ```\n   ❯ 1. consult-llm            ✔ connected\n   ```\n\n3. **Ask a question**:\n   > \"Consult Gemini about how to fix the race condition in server.ts\"\n\n## Example workflows\n\nSome real-world examples. Click to expand.\n\n<details>\n<summary>Explain the problem, and tell CC to consult a smarter LLM</summary>\n\n```\n  <Some long explanation of the issue here>\n\n  Consult LLM for assistance, use gemini\n\n⏺ I see the issue! The problem is that the frontend is trying to use the hint\n  for \"isn't\" but the server generated a hint for \"isn't it\" (the full phrase).\n  This is a mismatch between how the frontend segments text and how the server\n  generates hints.\n\n  Let me consult the LLM to get assistance on this segmentation mismatch issue.\n\n⏺ Write(hint-segmentation-issue.md)\n  ⎿  Wrote 53 lines to hint-segmentation-issue.md\n     # Hint Segmentation Mismatch Issue\n     ## Problem Description\n     There's a mismatch between how the frontend segments text for clickable\n     words and how the backend generates hints. This causes hint unlocking to\n     fail when the segments don't match exactly.\n     ## Example Case\n     **Original Text**: \"Hello! Nice weather, isn't it?\"\n     … +43 lines (ctrl+r to expand)\n\n⏺ consult-llm:consult_llm (MCP)(model: \"gemini-2.5-pro\", files: [\"/project/hint\n                                -segmentation-issue.md\",\"/project/web/src/compo\n                                nents/HintsText.tsx\",\"/project/web/src/utils/hi\n                                nts.ts\",\"/project/server/src/routes/public/hint\n                                s.ts\",\"/project/server/src/llm/hints.ts\"])\n  ⎿ An excellent analysis of the problem. The root cause has been correctly\n    identified: a race condition within the `handleConfirmGenerateHints`\n    function in `HintsText.tsx`. The component state updates in the background\n    after `await generateHints()`, but the function continues executing with a\n    stale `activeSegment` variable, causing it to request a hint for a smaller\n    text segment ( e.g., \"isn't\") instead of the full, newly-available hint\n    phrase (e.g., \"isn't it\").\n    … +607 lines (ctrl+r to expand)\n\n⏺ Perfect! The LLM identified the core issue - there's a race condition in\n  handleConfirmGenerateHints where the function uses stale state after async\n  operations. The solution is to re-target the hint key after generation to\n  find the correct matching hint.\n\n  Let me implement this solution:\n\n⏺ Update Todos\n  ⎿  ☐ Fix hint segmentation mismatch by implementing smarter hint key\n       targeting in handleConfirmGenerateHints\n```\n\n</details>\n\n<details>\n<summary>Using web mode to copy prompts to clipboard for browser-based LLMs</summary>\n\n```\n> I need help implementing a -b/--background flag for my Rust CLI. Use consult LLM with web mode\n\n⏺ I'll use web mode to copy the formatted prompt to your clipboard so you can\n  paste it into any browser-based LLM service.\n\n⏺ consult-llm:consult_llm (MCP)(prompt: \"What is the best approach to implement a\n                                 -b/--background flag for the \\\"workmux add\\\" command that\n                                 creates a tmux window in the background without switching to\n                                 it?\\n\\nThe current implementation automatically switches to\n                                 the newly created window via `tmux::select_window()` in the\n                                 `setup_environment()` function. I need to make this behavior\n                                 conditional based on a flag passed from the\n                                 CLI.\\n\\nConsider:\\n1. Where should the background flag be\n                                 threaded through the call chain?\\n2. Should I modify the\n                                 existing SetupOptions struct or add a parameter?\\n3. What's\n                                 the most maintainable approach given the codebase\n                                 structure?\\n4. Are there any edge cases to consider (e.g.,\n                                 should select_pane also be conditional)?\", files:\n                                 [\"src/cli.rs\",\"src/workflow.rs\",\"src/tmux.rs\"], web_mode:\n                                 true)\n  ⎿  ✓ Prompt copied to clipboard!\n\n     Please paste it into your browser-based LLM service and share the response\n     here before I proceed with any implementation.\n\n     Note: File paths were included:\n       - src/cli.rs\n       - src/workflow.rs\n       - src/tmux.rs\n\n> [User pastes the response from the external LLM]\n\n⏺ Thank you! Based on the recommendations, I'll now implement the solution...\n```\n\nThis is useful when:\n\n- You want to use a free browser-based LLM service instead of API credits\n- You prefer a specific LLM's web interface\n- You want to review the full prompt before submitting it\n\n</details>\n\n<details>\n<summary>Debugging Neovim plugin treesitter API breakage</summary>\n\n````\n⏺ Fixed! Added nil checks for both node and child and updated the iter_matches call to handle the newer API properly.\n\n> Still not working:\n\n  E5108: Error executing lua /Users/raine/code/testonly.nvim/lua/testonly.lua:92: attempt to call method 'child' (a nil value)\n  stack traceback:\n          /Users/raine/code/testonly.nvim/lua/testonly.lua:92: in function 'reset_all_exclusive'\n          /Users/raine/code/testonly.nvim/lua/testonly.lua:110: in function 'reset_all_exclusive'\n          [string \":lua\"]:1: in main chunk\n\n  Ask gemini\n\n⏺ Let me consult with a more powerful AI to understand the current Neovim treesitter API changes:\n\n⏺ consult-llm:consult_llm (MCP)(prompt: \"I'm debugging a Neovim plugin that's broken due to treesitter API changes. The error is \\\"attempt to call method 'child' (a nil value)\\\" on line 92. The\n                               code is trying to access child nodes from treesitter matches but the API seems to have changed in newer Neovim versions.\\n\\nHere's the problematic\n                               code:\\n```lua\\nfor _, match, _ in parsed_query:iter_matches(root, bufnr, start_row, end_row) do\\n    for id, node in pairs(match) do\\n        local name =\n                               parsed_query.captures[id]\\n        if name == \\\"call\\\" and node then\\n            local child = node:child(0)  -- ERROR HERE\\n            if child then\\n\n                                    local text = vim.treesitter.get_node_text(child, bufnr)\\n                -- ... rest of logic\\n            end\\n        end\\n    end\\nend\\n```\\n\\nWhat's the\n                               correct way to iterate over treesitter query matches and access child nodes in current Neovim versions? What API changes should I be aware of?\", files:\n                               [\"/Users/raine/code/testonly.nvim/lua/testonly.lua\"])\n  ⎿  Problem\n     -------\n\n     … +93 lines (ctrl+r to expand)\n\n⏺ Perfect! The issue is that iter_matches() can return nil nodes in the match table. Let me fix this by switching to the recommended iter_captures() approach:\n\n⏺ Update(lua/testonly.lua)\n  ⎿  Updated lua/testonly.lua with 7 additions and 10 removals\n       85      local root = parser:parse()[1]:root()\n       86      local start_row, _, end_row, _ = root:range()\n       87\n       88 -    for _, match, _ in parsed_query:iter_matches(root, bufnr, start_row, end_row) do\n       89 -      for id, node in pairs(match) do\n       90 -        local name = parsed_query.captures[id]\n       91 -        if name == \"call\" and node then\n       92 -          local child = node:child(0)\n       93 -          if child then\n       94 -            local text = vim.treesitter.get_node_text(child, bufnr)\n       88 +    for id, node, _ in parsed_query:iter_captures(root, bufnr, start_row, end_row) do\n       89 +      if parsed_query.captures[id] == \"call\" then\n       90 +        local first_child = node:named_child(0)\n       91 +        if first_child then\n       92 +          local text = vim.treesitter.get_node_text(first_child, bufnr)\n       93\n       94 -            if text == \"it.only\" or text == \"describe.only\" then\n       95 -              toggle_test_exclusive(child)\n       96 -            end\n       94 +          if text == \"it.only\" or text == \"describe.only\" then\n       95 +            toggle_test_exclusive(first_child)\n       96            end\n       97          end\n       98        end\n\n⏺ Fixed! The key changes:\n\n  1. Switched from iter_matches() to iter_captures() which never returns nil nodes\n  2. Used named_child(0) instead of child(0) to get only meaningful syntax nodes (not punctuation)\n  3. Simplified the iteration logic since iter_captures() directly gives us the node and capture ID\n\n  Your <leader>tr mapping should now work properly with the current Neovim treesitter API.\n````\n\n</details>\n\n<details>\n<summary>Using slash command to consult multiple LLMs for comparison</summary>\n\n```\n> /consult The branch completions should dynamic. Git should be called only when\nusing tab to autocomplete. Ask gemini and codex\n\n⏺ I'll consult both Gemini and Codex about making the branch completions dynamic (only\n  calling git when tab-completing). Let me gather the relevant context first.\n\n⏺ consult-llm - consult_llm (MCP)(prompt: \"I have a Rust CLI tool using clap that needs dynamic shell completions. Currently, the branch completions are generated at completion script\n                                 generation time via `possible_values()`. Instead, I need the git operations to only run when the user presses TAB to autocomplete.\\n\\nCurrent setup:\\n1.\n                                 `WorktreeBranchParser` implements `TypedValueParser` with `possible_values()` that calls git\\n2. Shell completions are generated via clap_complete's\n                                 `generate()` function\\n3. The completions are sourced at shell startup: `source <(workmux completions zsh)`\\n\\nThe problem: Running `workmux completions\n                                 zsh` takes ~210ms because it runs git operations during completion generation, not during actual tab completion.\\n\\nHow can I implement truly dynamic\n                                 completions where git is only called when the user presses TAB? What's the best approach with clap and clap_complete for zsh?\", model:\n                                 \"gemini-3-pro-preview\", files: [\"/Users/raine/code/workmux/src/cli.rs\",\"/Users/raine/code/workmux/src/git.rs\"], web_mode: false)\n  ⎿ The root cause of the slow startup (~210ms) is that `clap_complete::generate()` executes your `possible_values()` logic (git operations) *while generating the script*. This bake\n    s the *current* branches into the static script, which is both slow (happens every time you `source` it) and incorrect (branches become stale).\n    … +116 lines (ctrl+o to expand)\n\n\n⏺ consult-llm - consult_llm (MCP)(prompt: \"I have a Rust CLI tool using clap that needs dynamic shell completions. Currently, the branch completions are generated at completion script\n                                 generation time via `possible_values()`. Instead, I need the git operations to only run when the user presses TAB to autocomplete.\\n\\nCurrent setup:\\n1.\n                                 `WorktreeBranchParser` implements `TypedValueParser` with `possible_values()` that calls git\\n2. Shell completions are generated via clap_complete's\n                                 `generate()` function\\n3. The completions are sourced at shell startup: `source <(workmux completions zsh)`\\n\\nThe problem: Running `workmux completions\n                                 zsh` takes ~210ms because it runs git operations during completion generation, not during actual tab completion.\\n\\nHow can I implement truly dynamic\n                                 completions where git is only called when the user presses TAB? What's the best approach with clap and clap_complete for zsh?\", model:\n                                 \"gpt-5.1-codex-max\", files: [\"/Users/raine/code/workmux/src/cli.rs\",\"/Users/raine/code/workmux/src/git.rs\"], web_mode: false)\n  ⎿ **Findings**\n    - `WorktreeBranchParser::possible_values` (`cli.rs:41-72`) runs git during script generation, so `workmux completions zsh` always pays the git cost (~210ms) even though the resu\n    lt is only needed at tab time.\n    … +77 lines (ctrl+o to expand)\n\n\n⏺ Summary from Gemini and Codex\n\n  Both agree on the same solution:\n\n  Root Cause\n\n  clap_complete::generate() calls possible_values() during script generation, not at tab-completion time. This means git runs every shell startup.\n\n...\n```\n\nThis example shows using the `/consult` slash command to ask multiple LLMs\n(Gemini and Codex) about the same problem **in parallel** and compare their\nresponses. Both LLMs independently arrived at the same solution, providing\nconfidence in the approach.\n\n</details>\n\n## Backends\n\nEach model is routed to a **backend** — either an API endpoint or a CLI tool.\n\n| Backend        | Description                      | When to use                                                      |\n| -------------- | -------------------------------- | ---------------------------------------------------------------- |\n| **API**        | Queries LLM APIs directly        | You have API keys and want the simplest setup                    |\n| **Gemini CLI** | Shells out to `gemini` CLI       | Free quota (Gemini), existing subscriptions, or prefer CLI tools |\n| **Codex CLI**  | Shells out to `codex` CLI        | OpenAI models via Codex subscription                             |\n| **Cursor CLI** | Shells out to `cursor-agent` CLI | Route GPT and Gemini through one tool                            |\n| **OpenCode CLI** | Shells out to `opencode` CLI   | Use Copilot subscription, OpenCode's 75+ providers               |\n| **Web**        | Copies prompt to clipboard       | You prefer browser UIs or want to review prompts                 |\n\n### API (default)\n\nThe default backend. Requires API keys configured via environment variables. See\n[Configuration](#configuration) for details.\n\n### CLI backends\n\nInstead of making API calls, shell out to local CLI tools. The CLI tools can\nexplore the codebase themselves, so you don't need to pass all relevant files as\ncontext, but it helps.\n\n#### Gemini CLI\n\nUse Gemini's local CLI to take advantage of Google's\n[free quota](https://developers.google.com/gemini-code-assist/resources/quotas#quotas-for-agent-mode-gemini-cli)\nor use your Google AI Pro subscription.\n\n**Requirements:**\n\n1. Install the [Gemini CLI](https://github.com/google-gemini/gemini-cli)\n2. Authenticate via `gemini login`\n\n**Setup:**\n\n```bash\nclaude mcp add consult-llm -e CONSULT_LLM_GEMINI_BACKEND=gemini-cli -- npx -y consult-llm-mcp\n```\n\n#### Codex CLI\n\nUse OpenAI's Codex CLI for OpenAI models.\n\n**Requirements:**\n\n1. Install the Codex CLI\n2. Authenticate via `codex login`\n\n**Setup:**\n\n```bash\nclaude mcp add consult-llm -e CONSULT_LLM_OPENAI_BACKEND=codex-cli -- npx -y consult-llm-mcp\n```\n\n<!-- prettier-ignore -->\n> [!TIP]\n> Reasoning effort defaults to `high`. Override with\n> `-e CONSULT_LLM_CODEX_REASONING_EFFORT=xhigh`. Options: `none`, `minimal`,\n> `low`, `medium`, `high`, `xhigh`.\n\n#### Cursor CLI\n\nUse Cursor's agent CLI to route GPT and Gemini models through one tool.\n\n**Requirements:**\n\n1. Install the [Cursor agent CLI](https://cursor.com/cli) (`cursor-agent` in\n   PATH)\n\n**Setup:**\n\n```bash\n# Route GPT models through Cursor CLI\nclaude mcp add consult-llm -e CONSULT_LLM_OPENAI_BACKEND=cursor-cli -- npx -y consult-llm-mcp\n\n# Route Gemini models through Cursor CLI\nclaude mcp add consult-llm -e CONSULT_LLM_GEMINI_BACKEND=cursor-cli -- npx -y consult-llm-mcp\n\n# Route everything through Cursor CLI\nclaude mcp add consult-llm \\\n  -e CONSULT_LLM_OPENAI_BACKEND=cursor-cli \\\n  -e CONSULT_LLM_GEMINI_BACKEND=cursor-cli \\\n  -e CONSULT_LLM_ALLOWED_MODELS=\"gemini-3.1-pro-preview,gpt-5.3-codex\" \\\n  -- npx -y consult-llm-mcp\n```\n\n**Shell command permissions:**\n\nCursor CLI runs with `--mode ask`, which blocks shell commands by default. If\nyour prompts involve tools that need to run commands (e.g. `git diff` for code\nreview), allow them in `~/.cursor/cli-config.json`:\n\n```json\n{\n  \"permissions\": {\n    \"allow\": [\"Shell(git diff*)\", \"Shell(git log*)\", \"Shell(git show*)\"],\n    \"deny\": []\n  }\n}\n```\n\nGlob patterns are supported. The `deny` list takes precedence over `allow`.\n\n#### OpenCode CLI\n\nUse [OpenCode](https://opencode.ai) as a backend to route models through any of\nits 75+ supported providers — including GitHub Copilot, OpenRouter, and local\nmodels via Ollama.\n\n**Requirements:**\n\n1. Install [OpenCode](https://opencode.ai/docs/installation/)\n2. Configure providers via `opencode providers`\n\n**Setup:**\n\n```bash\n# Route MiniMax models through OpenCode\nclaude mcp add consult-llm \\\n  -e CONSULT_LLM_MINIMAX_BACKEND=opencode \\\n  -- npx -y consult-llm-mcp\n\n# Route OpenAI models through Copilot subscription\nclaude mcp add consult-llm \\\n  -e CONSULT_LLM_OPENAI_BACKEND=opencode \\\n  -e CONSULT_LLM_OPENCODE_OPENAI_PROVIDER=copilot \\\n  -- npx -y consult-llm-mcp\n\n# Route everything through OpenCode\nclaude mcp add consult-llm \\\n  -e CONSULT_LLM_OPENAI_BACKEND=opencode \\\n  -e CONSULT_LLM_GEMINI_BACKEND=opencode \\\n  -e CONSULT_LLM_DEEPSEEK_BACKEND=opencode \\\n  -e CONSULT_LLM_MINIMAX_BACKEND=opencode \\\n  -- npx -y consult-llm-mcp\n```\n\nThe executor maps model IDs to OpenCode's `provider/model` format automatically.\nFor example, `MiniMax-M2.7` becomes `opencode run --model minimax/MiniMax-M2.7`.\n\n**Provider prefix overrides:**\n\nBy default, each provider family maps to its natural OpenCode provider ID\n(`openai`, `google`, `deepseek`, `minimax`). Override with per-family env vars\nwhen you want to route through a different OpenCode provider:\n\n- `CONSULT_LLM_OPENCODE_OPENAI_PROVIDER` — default: `openai`\n- `CONSULT_LLM_OPENCODE_GEMINI_PROVIDER` — default: `google`\n- `CONSULT_LLM_OPENCODE_DEEPSEEK_PROVIDER` — default: `deepseek`\n- `CONSULT_LLM_OPENCODE_MINIMAX_PROVIDER` — default: `minimax`\n- `CONSULT_LLM_OPENCODE_PROVIDER` — global fallback for all families\n\nFor example, `CONSULT_LLM_OPENCODE_OPENAI_PROVIDER=copilot` turns\n`gpt-5.2` into `opencode run --model copilot/gpt-5.2`.\n\n#### Multi-turn conversations\n\nCLI backends support multi-turn conversations via the `thread_id` parameter. The\nfirst response includes a `[thread_id:xxx]` prefix. Pass that ID in follow-up\nrequests to continue the conversation with full context from prior turns.\n\nThis works with all CLI backends (Gemini CLI, Codex CLI, Cursor CLI). Each uses\nits own session/thread mechanism, but all are passed through the same\n`thread_id` parameter.\n\n```\n> Ask codex what's the best caching strategy for our read-heavy API\n\n⏺ consult-llm - consult_llm (MCP)(prompt: \"What's the best caching strategy for a read-heavy API?\",\n                                   model: \"gpt-5.3-codex\")\n  ⎿  [thread_id:thread_b1ff711...]\n\n     Redis with TTL-based invalidation. Set short TTLs for volatile data ...\n\n⏺ Codex recommends Redis with TTL-based invalidation — but our payment\n  endpoints need strong consistency. Let me push back on that.\n\n⏺ consult-llm - consult_llm (MCP)(prompt: \"What if we need strong consistency guarantees?\n                                            Does that change your recommendation?\",\n                                   model: \"gpt-5.3-codex\",\n                                   thread_id: \"thread_b1ff711...\")\n  ⎿  [thread_id:thread_b1ff711...]\n\n     Yes — with strong consistency, the TTL approach breaks down. Consider\n     write-through caching or cache invalidation on writes instead ...\n\n⏺ Good — Codex revised its recommendation based on the consistency\n  constraint. I'll use write-through caching for the payment endpoints.\n```\n\nSee the [debate skills](#debate-skills) for multi-LLM workflows that use\n`thread_id` to maintain context across debate rounds.\n\n### Web mode\n\nCopies the formatted prompt to clipboard instead of querying an LLM. Paste into\nany browser-based LLM (ChatGPT, Claude.ai, Gemini, etc.).\n\n**When to use:** Prefer a specific web UI, want to review the prompt first, or\ndon't have API keys.\n\n**Workflow:**\n\n1. Ask Claude to \"use consult LLM with web mode\"\n2. Paste into your browser-based LLM\n3. Paste the response back into Claude Code\n\nSee the \"Using web mode...\" example above for a concrete transcript.\n\n## Configuration\n\n### Environment variables\n\n- `OPENAI_API_KEY` - Your OpenAI API key (required for OpenAI models in API\n  mode)\n- `GEMINI_API_KEY` - Your Google AI API key (required for Gemini models in API\n  mode)\n- `DEEPSEEK_API_KEY` - Your DeepSeek API key (required for DeepSeek models)\n- `MINIMAX_API_KEY` - Your MiniMax API key (required for MiniMax models)\n- `ANTHROPIC_API_KEY` - Your Anthropic API key (required for Claude models)\n- `CONSULT_LLM_DEFAULT_MODEL` - Override the default model (optional)\n  - Accepts selectors (`gemini`, `openai`, `anthropic`, `deepseek`, `minimax`)\n    or exact model IDs\n    (`gpt-5.4`, `gemini-3.1-pro-preview`, `claude-opus-4-7`, etc.)\n  - Selectors are resolved to the best available model at startup\n- `CONSULT_LLM_GEMINI_BACKEND` - Backend for Gemini models (optional)\n  - Options: `api` (default), `gemini-cli`, `cursor-cli`, `opencode`\n- `CONSULT_LLM_OPENAI_BACKEND` - Backend for OpenAI models (optional)\n  - Options: `api` (default), `codex-cli`, `cursor-cli`, `opencode`\n- `CONSULT_LLM_DEEPSEEK_BACKEND` - Backend for DeepSeek models (optional)\n  - Options: `api` (default), `opencode`\n- `CONSULT_LLM_MINIMAX_BACKEND` - Backend for MiniMax models (optional)\n  - Options: `api` (default), `opencode`\n- `CONSULT_LLM_ANTHROPIC_BACKEND` - Backend for Anthropic models (optional)\n  - Options: `api` (default)\n- `CONSULT_LLM_ALLOWED_MODELS` - Restrict which concrete models can be used\n  (optional)\n  - Comma-separated list, e.g., `gpt-5.4,gemini-3.1-pro-preview`\n  - Selectors resolve against this list — e.g., if only `gemini-2.5-pro` is\n    allowed, the `gemini` selector resolves to it\n  - Useful when a backend doesn't support all models (e.g., Cursor CLI)\n  - See [Tips](#controlling-which-models-are-used) for usage examples\n- `CONSULT_LLM_EXTRA_MODELS` - Add models not in the built-in list (optional)\n  - Comma-separated list, e.g., `grok-3,kimi-k2.5`\n  - Merged with built-in models and included in the tool schema\n  - Useful for newly released models with a known provider prefix (`gpt-`,\n    `gemini-`, `deepseek-`, `MiniMax-`, `claude-`)\n- `CONSULT_LLM_CODEX_REASONING_EFFORT` - Configure reasoning effort for Codex\n  CLI (optional, default: `high`)\n  - See [Codex CLI](#codex-cli) for details and available options\n- `CONSULT_LLM_OPENCODE_PROVIDER` - Global OpenCode provider prefix (optional)\n  - Overrides the default provider ID for all families when using the `opencode`\n    backend\n  - See [OpenCode CLI](#opencode-cli) for details and per-family overrides\n- `CONSULT_LLM_SYSTEM_PROMPT_PATH` - Custom path to system prompt file\n  (optional)\n  - Overrides the default `~/.consult-llm-mcp/SYSTEM_PROMPT.md` location\n  - Useful for project-specific prompts\n- `CONSULT_LLM_NO_UPDATE_CHECK` - Disable automatic update checking on server\n  startup (optional)\n  - Set to `1` to disable\n  - By default, the server checks for new versions in the background every 24\n    hours and logs a notice when an update is available\n  - Only applies to binary installs — npm installs are never checked\n- `MCP_DEBUG_STDIN` - Log raw JSON-RPC messages received on stdin (optional)\n  - Set to `1` to enable\n  - Logs every message as `RAW RECV` entries and poll timing gaps as\n    `STDIN POLL` entries in `mcp.log`\n  - Useful for debugging transport-level issues\n\n### Custom system prompt\n\nYou can customize the system prompt used when consulting LLMs by creating a\n`SYSTEM_PROMPT.md` file in `~/.consult-llm-mcp/`:\n\n```bash\nnpx consult-llm-mcp init-prompt\n```\n\nThis creates a placeholder file with the default system prompt that you can edit\nto customize how the consultant LLM behaves. The custom prompt is read on every\nrequest, so changes take effect immediately without restarting the server.\n\nWhen a custom prompt file exists, it acts as a full override — `task_mode`\noverlays are not applied on top. To revert to the default prompt with\n`task_mode` support, simply delete the `SYSTEM_PROMPT.md` file.\n\n#### Custom prompt path\n\nUse `CONSULT_LLM_SYSTEM_PROMPT_PATH` to override the default prompt file\nlocation. This is useful for project-specific prompts that you can commit to\nyour repository:\n\n```bash\nclaude mcp add consult-llm \\\n  -e GEMINI_API_KEY=your_key \\\n  -e CONSULT_LLM_SYSTEM_PROMPT_PATH=/path/to/project/.consult-llm-mcp/SYSTEM_PROMPT.md \\\n  -- npx -y consult-llm-mcp\n```\n\n## Tips\n\n### Controlling which models are used\n\nThe `model` parameter accepts **selectors** (`gemini`, `openai`, `anthropic`,\n`deepseek`) that the server resolves to the best available concrete model. When\nno model is specified, the server uses `CONSULT_LLM_DEFAULT_MODEL` or its\nbuilt-in fallback.\n\n**Selector resolution order** (first available wins):\n\n| Selector    | Priority                                                       |\n| ----------- | -------------------------------------------------------------- |\n| `gemini`    | gemini-3.1-pro-preview → gemini-3-pro-preview → gemini-2.5-pro |\n| `openai`    | gpt-5.5 → gpt-5.4 → gpt-5.3-codex → gpt-5.2 → gpt-5.2-codex    |\n| `anthropic` | claude-opus-4-7                                                |\n| `deepseek`  | deepseek-v4-pro                                                |\n\n**Restricting models with `CONSULT_LLM_ALLOWED_MODELS`:**\n\nIf your backend doesn't support all models (e.g., Cursor CLI can't use\n`gpt-5.4`), use `CONSULT_LLM_ALLOWED_MODELS` to filter. Selectors will\nautomatically resolve to the best model within the allowed list:\n\n```bash\n# Only allow codex models through Cursor CLI\nclaude mcp add consult-llm \\\n  -e CONSULT_LLM_OPENAI_BACKEND=cursor-cli \\\n  -e CONSULT_LLM_ALLOWED_MODELS='gpt-5.3-codex,gemini-3.1-pro-preview' \\\n  -- npx -y consult-llm-mcp\n# \"openai\" selector → gpt-5.3-codex (gpt-5.4 filtered out)\n```\n\n## MCP tool: consult_llm\n\nThe server provides a single tool called `consult_llm` for asking powerful AI\nmodels complex questions.\n\n### Parameters\n\n- **prompt** (required): Your question or request for the consultant LLM\n\n- **files** (optional): Array of file paths to include as context\n  - All files are added as context with file paths and code blocks\n\n- **model** (optional): Model selector or exact model ID\n  - Selectors: `gemini`, `openai`, `anthropic`, `deepseek` — the server resolves\n    to the best available model for each family\n  - Exact model IDs (`gpt-5.4`, `gemini-3.1-pro-preview`, `claude-opus-4-7`,\n    etc.) are also accepted as an advanced override\n  - When omitted, the server uses the configured default\n\n- **task_mode** (optional): Controls the system prompt persona. The calling LLM\n  should choose based on the task:\n  - `general` (default): Neutral base prompt that defers to the user prompt\n  - `review`: Critical code reviewer — bugs, security, performance,\n    anti-patterns\n  - `debug`: Focused troubleshooter — root cause analysis, execution tracing,\n    ignores style issues\n  - `plan`: Constructive architect — trade-offs, alternatives, always includes a\n    final recommendation\n  - `create`: Generative writer — docs, content, polished output\n\n- **web_mode** (optional): Copy prompt to clipboard instead of querying LLM\n  - Default: `false`\n  - When `true`, the formatted prompt (including system prompt and file\n    contents) is copied to clipboard for manual pasting into browser-based LLM\n    services\n\n- **thread_id** (optional): Resume a multi-turn conversation\n  - Works with CLI backends (Codex CLI, Gemini CLI, Cursor CLI)\n  - The first response includes a `[thread_id:xxx]` prefix — pass that ID back\n    as `thread_id` in follow-up requests to maintain conversation context\n\n- **git_diff** (optional): Include git diff output as context\n  - **files** (required): Specific files to include in diff\n  - **repo_path** (optional): Path to git repository (defaults to current\n    directory)\n  - **base_ref** (optional): Git reference to compare against (defaults to HEAD)\n\n## Supported models\n\n- **gemini-2.5-pro**: Google's Gemini 2.5 Pro\n- **gemini-3-pro-preview**: Google's Gemini 3 Pro Preview\n- **gemini-3.1-pro-preview**: Google's Gemini 3.1 Pro Preview\n- **deepseek-v4-pro**: DeepSeek's V4 Pro reasoning model\n- **MiniMax-M2.7**: MiniMax's M2.7 reasoning model (204K context)\n- **gpt-5.5**: OpenAI's GPT-5.5 model\n- **gpt-5.4**: OpenAI's GPT-5.4 model\n- **gpt-5.2**: OpenAI's GPT-5.2 model\n- **gpt-5.3-codex**: OpenAI's Codex model based on GPT-5.3\n- **gpt-5.2-codex**: OpenAI's Codex model based on GPT-5.2\n- **claude-opus-4-7**: Anthropic's Claude Opus 4.7\n\n## Logging\n\nAll prompts and responses are logged to\n`$XDG_STATE_HOME/consult-llm-mcp/mcp.log` (defaults to\n`~/.local/state/consult-llm-mcp/mcp.log`) with:\n\n- Tool call parameters\n- Full prompts and responses\n- Token usage and cost estimates\n\n<details>\n<summary>Example</summary>\n\n```\n[2025-06-22T20:16:04.673Z] TOOL CALL: consult_llm\nArguments: {\n  \"files\": [\n    \"refactor-analysis.md\",\n    \"src/main.ts\",\n    \"src/schema.ts\",\n    \"src/config.ts\",\n    \"src/llm.ts\",\n    \"src/llm-cost.ts\"\n  ],\n  \"model\": \"deepseek-reasoner\"\n}\n================================================================================\n[2025-06-22T20:16:04.675Z] PROMPT (model: deepseek-reasoner):\n## Relevant Files\n\n### File: src/main.ts\n\n...\n\nPlease provide specific suggestions for refactoring with example code structure\nwhere helpful.\n================================================================================\n[2025-06-22T20:19:20.632Z] RESPONSE (model: deepseek-reasoner):\nBased on the analysis, here are the key refactoring suggestions to improve\nseparation of concerns and maintainability:\n\n...\n\nThis refactoring maintains all existing functionality while significantly\nimproving maintainability and separation of concerns. The new structure makes\nit easier to add features like new LLM providers, additional context sources,\nor alternative prompt formats.\n\nTokens: 3440 input, 5880 output | Cost: $0.014769 (input: $0.001892, output: $0.012877)\n```\n\n</details>\n\n## Monitor\n\n`consult-llm-monitor` is a real-time TUI dashboard for watching active\nconsultations across all running MCP server instances. It shows what's being\nconsulted, which models are in use, and how long each request takes.\n\n<p align=\"center\">\n  <img src=\"meta/monitor-demo.gif\" alt=\"consult-llm-monitor demo\" width=\"800\">\n</p>\n\nThe monitor binary is included when you install via the install script (same\nscript that installs `consult-llm-mcp`).\n\n```bash\nconsult-llm-monitor\n```\n\nThe main **table view** shows two panels: active server instances with their\nin-flight consultations, and a history log of completed consultations with\ntimestamps, models, durations, and token counts.\n\nPress `Enter` on any consultation to open the **detail view** with the full\nevent log - prompt, response with syntax-highlighted markdown, tool calls, and\ntoken usage. Press `?` for keyboard shortcuts.\n\n## Activation methods\n\n### 1. No custom activation (simplest)\n\nWhen you add an MCP to Claude Code, the tool's schema is injected into the\nagent's context. This allows Claude to infer when to call the MCP from natural\nlanguage (e.g., \"ask gemini about...\"). Works out of the box, but you have less\ncontrol over how the MCP is invoked.\n\n### 2. Skills\n\nAutomatically triggers when Claude detects matching intent. Like slash commands,\nsupports custom instructions (e.g., always gathering relevant files), but not\nalways reliably triggered. See the [consult skill](#consult) below.\n\n**Recommendation:** Start with no custom activation. Use skills if you need\ncustom instructions for how the MCP is invoked.\n\n## Skills\n\n### Installing skills\n\nInstall all skills globally with a single command:\n\n```bash\ncurl -fsSL https://raw.githubusercontent.com/raine/consult-llm-mcp/main/scripts/install-skills | bash\n```\n\nThis installs skills for all detected platforms:\n\n- **Claude Code** → `~/.claude/skills/`\n- **OpenCode** → `~/.config/opencode/skills/`\n- **Codex** → `~/.codex/skills/`\n\nTo uninstall:\n\n```bash\ncurl -fsSL https://raw.githubusercontent.com/raine/consult-llm-mcp/main/scripts/install-skills | bash -s uninstall\n```\n\n### consult\n\nAn example [Claude Code skill](https://code.claude.com/docs/en/skills) that uses\nthe `consult_llm` MCP tool to create commands like \"ask gemini\" or \"ask codex\".\nSee [skills/consult/SKILL.md](skills/consult/SKILL.md) for the full content.\n\nType \"ask gemini about X\" or \"ask codex about X\" in Claude Code. This is not\nstrictly necessary since Claude can infer from the schema that \"ask gemini\"\nshould call this MCP, but it gives more precise control over how the agent calls\nthis MCP.\n\n### collab\n\n**Collaborative ideation.** Gemini and Codex independently brainstorm ideas,\nthen build on each other's suggestions across multiple rounds. Unlike debate,\nthe tone is cooperative — refining and combining rather than critiquing. Claude\nsynthesizes the strongest ideas into a plan and implements. See\n[skills/collab/SKILL.md](skills/collab/SKILL.md).\n\n```\n> /collab how should we handle offline sync for the mobile app\n```\n\n### collab-vs\n\n**Claude brainstorms with one LLM.** Claude and an opponent (Gemini or Codex)\ntake turns building on each other's ideas. Like collab, but Claude participates\ndirectly instead of moderating. See\n[skills/collab-vs/SKILL.md](skills/collab-vs/SKILL.md).\n\n```\n> /collab-vs --gemini how should we handle offline sync for the mobile app\n```\n\n### debate\n\n**Claude moderates, two LLMs debate.** Gemini and Codex independently propose\napproaches, then critique each other's proposals. Claude synthesizes the best\nideas and implements. See [skills/debate/SKILL.md](skills/debate/SKILL.md).\n\n```\n> /debate design the multi-tenant isolation strategy\n```\n\n### debate-vs\n\n**Claude participates as a debater** against one opponent LLM (Gemini or Codex)\nthrough multiple rounds. Claude forms its own position, then debates back and\nforth before synthesizing and implementing. See\n[skills/debate-vs/SKILL.md](skills/debate-vs/SKILL.md).\n\n```\n> /debate-vs --gemini design the multi-tenant isolation strategy\n```\n\n## Updating\n\n**Binary installs:**\n\n```bash\nconsult-llm-mcp update\n```\n\nDownloads the latest release from GitHub with SHA-256 checksum verification. If\n`consult-llm-monitor` is found alongside the binary, it's updated too.\n\nThe server also checks for updates in the background on startup (every 24 hours)\nand logs a notice when a newer version is available. Disable with\n`CONSULT_LLM_NO_UPDATE_CHECK=1`.\n\n## Why MCP and not CLI?\n\nThe server maps one `model` parameter onto five backends (OpenAI API, Gemini\nAPI, Gemini CLI, Codex CLI, Cursor CLI) with different commands, streaming\nformats, output schemas, file handling, and resume semantics. Doing this through\nagent Bash calls would push all of that per-provider plumbing into the agent or\na wrapper script/CLI.\n\nMCP also sidesteps shell escaping. Prompts contain code with backticks, `$`, and\nquotes. Passing one model's code-heavy response into another call breaks bash\nquoting and requires temp files. MCP passes structured JSON instead.\n\nMulti-turn workflows add more friction as a CLI. To continue a conversation, the\nagent needs to find a session ID in the CLI's output and pass it back as a flag\non the next invocation. With MCP, the agent passes `thread_id` as a parameter\nand the server handles the provider-specific resume mechanics internally.\n\nThe MCP tool is also easier to compose into [skills](#skills). `/consult`,\n`/collab`, and `/debate` all just say \"call `consult_llm` with these\nparameters.\" A CLI version would need each skill to either teach the agent the\nCLI's interface or reference a separate skill that does. A skill that\norchestrates a multi-model debate is ~90 lines with MCP. As shell commands, the\nsame skill would either balloon into hundreds of lines of escaping rules and\nstdout parsing, or depend on another skill that teaches the agent how to call\neach CLI.\n\nIf you only need a single provider with simple prompts, a Bash call to `gemini`\nor `codex` with some `jq` filtering will work fine. MCP starts to make more\nsense with multiple backends, multi-turn conversations across providers, or\ncustom workflows that nicely compose on top.\n\n## Development\n\nTo work on the MCP server locally and use your development version:\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/raine/consult-llm-mcp.git\n   cd consult-llm-mcp\n   ```\n\n2. Build and test:\n\n   ```bash\n   cargo build\n   cargo test\n   just check  # format, lint, test\n   ```\n\n3. Add the MCP server to Claude Code using your local build:\n   ```bash\n   claude mcp add consult-llm -- /path/to/consult-llm-mcp/target/debug/consult-llm-mcp\n   ```\n\nNow when you make changes, rebuild with `cargo build` and restart Claude Code.\n\n### Releasing\n\n```bash\nscripts/publish patch  # or minor, major\n```\n\nThis bumps the version in `package.json` and `Cargo.toml`, commits, tags, and\npushes. GitHub Actions handles cross-compilation and npm publishing.\n\n## Related projects\n\n- [workmux](https://github.com/raine/workmux) — Git worktrees + tmux windows for\n  parallel AI agent workflows\n- [claude-history](https://github.com/raine/claude-history) — Search and view\n  Claude Code conversation history with fzf\n- [tmux-file-picker](https://github.com/raine/tmux-file-picker) — Pop up fzf in\n  tmux to quickly insert file paths, perfect for AI coding assistants\n- [tmux-agent-usage](https://github.com/raine/tmux-agent-usage) — Display AI agent\n  rate limit usage in your tmux status bar\n","readmeFilename":"README.md"}