Ask LLM

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Package	Type	Version	Downloads
ask-gemini-mcp	MCP Server
ask-codex-mcp	MCP Server
ask-ollama-mcp	MCP Server
ask-antigravity-mcp	MCP Server
ask-llm-mcp	MCP Server
@ask-llm/plugin	Claude Code Plugin		/plugin install

MCP servers + Claude Code plugin for AI-to-AI collaboration

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Get a second opinion before you ship. Ask LLM lets your AI assistant — Claude Code, Cursor, Claude Desktop, or any of 40+ MCP clients — consult a _second_ model to review your code, debate a plan, or catch a bug it might have missed. Pick the reviewer that fits: OpenAI Codex (GPT-5.5), Google Antigravity (agy), a local Ollama model, or Gemini (1M+ token context). Standard MCP, no prompt hacks.

⚠️ Gemini CLI goes enterprise-only on 2026-06-18: From that date Google restricts Gemini CLI to Gemini Code Assist Standard/Enterprise seats, and free, Google AI Pro, and Ultra accounts lose access. ask-gemini-mcp still installs, but a non-enterprise account then surfaces actionable guidance instead of output. Free/Pro users: switch to `ask-antigravity` (the Google-sanctioned successor, subscription-backed via Google AI Pro/Ultra), `ask-codex`, or `ask-ollama`. Announcement

Why a second opinion?

Your primary AI is confident — but confidence isn't correctness. A second model, with no stake in the first one's answer, catches what it missed.

• Second opinion on code — before you commit to an approach, have another model review it independently.
• Debate a plan — send an architecture proposal for critique, alternatives, and trade-off analysis.
• Review a diff — have a different model analyze your changes to surface issues your primary AI glossed over.
• Read more than fits — Gemini and Antigravity's large context windows ingest whole codebases at once.
• Keep it local — run reviews through Ollama when nothing can leave your machine.

In action

You:    ask codex to review src/auth.ts for security issues
Codex:  ⚠ verifyToken() compares tokens with === — not timing-safe (line 42)
        ⚠ the session cookie is missing a SameSite attribute
Claude: Good catches — applying both fixes to src/auth.ts.

One prompt. A second model reviews independently; your assistant applies the fix — no copy-paste between tools.

Quick Start

Claude Code

# All-in-one — auto-detects installed providers
claude mcp add --scope user ask-llm -- npx -y ask-llm-mcp

<details> <summary>Or install providers individually</summary>

claude mcp add --scope user gemini -- npx -y ask-gemini-mcp
claude mcp add --scope user codex -- npx -y ask-codex-mcp
claude mcp add --scope user ollama -- npx -y ask-ollama-mcp
claude mcp add --scope user antigravity -- npx -y ask-antigravity-mcp

</details>

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "ask-llm": {
      "command": "npx",
      "args": ["-y", "ask-llm-mcp"]
    }
  }
}

<details> <summary>Or install providers individually</summary>

{
  "mcpServers": {
    "gemini": {
      "command": "npx",
      "args": ["-y", "ask-gemini-mcp"]
    },
    "codex": {
      "command": "npx",
      "args": ["-y", "ask-codex-mcp"]
    },
    "ollama": {
      "command": "npx",
      "args": ["-y", "ask-ollama-mcp"]
    }
  }
}

</details>

<details> <summary>Cursor, Codex CLI, OpenCode, and other clients</summary>

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "ask-llm": { "command": "npx", "args": ["-y", "ask-llm-mcp"] }
  }
}

Codex CLI (~/.codex/config.toml):

[mcp_servers.ask-llm]
command = "npx"
args = ["-y", "ask-llm-mcp"]

Any MCP Client (STDIO transport):

{ "command": "npx", "args": ["-y", "ask-llm-mcp"] }

Replace ask-llm-mcp with ask-codex-mcp, ask-antigravity-mcp, ask-ollama-mcp, or ask-gemini-mcp for a single provider.

</details>

Choose your reviewer

Claude Code Plugin

The Ask LLM plugin adds multi-provider code review, brainstorming, and automated hooks directly into Claude Code:

/plugin marketplace add Lykhoyda/ask-llm
/plugin install ask-llm@ask-llm-plugins

What You Get

The review agents use a 4-phase pipeline inspired by Anthropic's code-review plugin: context gathering, prompt construction with explicit false-positive exclusions, synthesis, and source-level validation of each finding.

See the plugin docs for details.

Prerequisites

• [Node.js](https://nodejs.org/) v20.0.0 or higher (LTS)
• At least one provider:
• Codex CLI — installed and authenticated
• Antigravity CLI (agy) — installed and logged in once (Google AI Pro/Ultra)
• Ollama — running locally with a model pulled (ollama pull qwen3.6:27b)
• Gemini CLI — npm install -g @google/gemini-cli && gemini login (enterprise-gated from 2026-06-18)

MCP Tools

All ask-* tools accept an optional sessionId parameter for multi-turn conversations and now return a structured AskResponse (provider, response, model, sessionId, usage) via MCP outputSchema alongside the human-readable text. The orchestrator (ask-llm-mcp) also exposes usage://current-session as an MCP Resource for live JSON snapshots.

Usage Examples

ask codex to review the changes in src/auth.ts for security issues
ask antigravity to debate this architecture plan in docs/design.md
ask ollama to explain src/config.ts (runs locally, no data sent anywhere)
ask gemini to summarize @. the current directory (1M+ context, @ is Gemini-only)
use multi-llm to compare what codex and gemini think about this approach

CLI Subcommands

The orchestrator binary (ask-llm-mcp) supports two CLI modes alongside the default MCP server:

# Interactive multi-provider REPL — switch providers, persist sessions, see usage live
npx ask-llm-mcp repl

# Diagnose your setup — Node version, PATH, provider CLI versions, env vars
npx ask-llm-mcp doctor          # human-readable
npx ask-llm-mcp doctor --json   # machine-readable, exit 1 on error

The REPL ships sessions per provider (/provider gemini, /provider codex, /new, /sessions, /usage) and inherits all the executor behavior (quota fallback, stream-json output for Gemini, native session resume).

Models

Gemini and Codex automatically fall back to a lighter model on quota errors. Ollama runs locally and never substitutes a model — if the requested model isn't pulled, it returns a clear ollama pull error.

Documentation

• Docs site: lykhoyda.github.io/ask-llm
• AI-readable: llms.txt | llms-full.txt

Contributing

Contributions are welcome! See open issues for things to work on.

License

MIT License. See LICENSE for details.

Disclaimer: This is an unofficial, third-party tool and is not affiliated with, endorsed, or sponsored by Google or OpenAI.