mcp-cli-tools

MCP server that gives AI coding agents (Claude Code, Cursor, Cline, etc.) safe access to multiple AI models through Antigravity CLI and OpenAI Codex CLI.

Think of it as a local Fugu-style orchestrator: your AI agent can consult Gemini, GPT, and other models mid-conversation, compare answers, ask for adversarial review, and run scoped code reviews without leaving the editor.

What it does

Your AI agent gains the ability to:

• Get a second opinion from a different model family

• Use a single Fugu-style tool that hides routing, decomposition, worker calls, and final synthesis

• Compare multiple external answers in one tool call

• Run code reviews via Codex without leaving the conversation

• Do adversarial verification (one model checks another's work)

• Route by expertise — Gemini for analysis, GPT for code review

• Receive structured metadata for each call: command, cwd, exit code, timeout, duration, stdout, stderr

Related MCP server: codex-fusion-mcp

Prerequisites

• Antigravity CLI installed as agy

• OpenAI Codex CLI installed as codex

• Node.js 18+

Install

git clone https://github.com/Marcelo-Henry/mcp-cli-tools.git
cd mcp-cli-tools
npm install
npm run build

Configure in Claude Code

Add to ~/.claude/settings.json under mcpServers:

{
  "mcpServers": {
    "cli-tools": {
      "command": "node",
      "args": ["/path/to/mcp-cli-tools/dist/index.js"]
    }
  }
}

Usage

fugu_orchestrate

Use this as the main entry point when you want behavior closest to Fugu/Fugu-Ultra.

Fast mode selects one worker for lower latency:

fugu_orchestrate(
  task: "Explain this TypeScript error and suggest the smallest fix",
  mode: "fast",
  effort: "standard",
  cwd: "/path/to/repo"
)

Ultra mode builds and executes a workflow with model_id, subtasks, access_list, critique, and synthesis:

fugu_orchestrate(
  task: "Create a local task dashboard from scratch with persistence, tests, and a polished UI",
  mode: "ultra",
  effort: "max",
  sharedMemoryKey: "taskpulse",
  cwd: "/path/to/repo"
)

Restrict the worker pool when privacy, cost, or compliance matters:

fugu_orchestrate(
  task: "Analyze this architecture",
  mode: "ultra",
  excludeModels: ["gpt-oss-120b"]
)

fugu_orchestrate is a local deterministic orchestration layer inspired by the public Fugu/Fugu-Ultra workflow shape. It is not Sakana's learned orchestrator model.

consult_model

Ask one external model:

consult_model(prompt: "Analyze this architecture", model: "gemini-3.1-pro", cwd: "/path/to/repo")

compare_models

Ask multiple models in parallel:

compare_models(
  prompt: "Which migration strategy is safest for this repo?",
  models: ["gemini-3.5-flash", "gemini-3.1-pro"]
)

adversarial_check

Ask an external model to find flaws in a proposal:

adversarial_check(
  proposal: "Move all orchestration policy into CLAUDE.md",
  context: "MCP server for Claude Code + GPT/Gemini collaboration"
)

review_workspace

Review uncommitted changes:

review_workspace(scope: "uncommitted", cwd: "/path/to/repo")

Review against a branch:

review_workspace(scope: "base", base: "main", cwd: "/path/to/repo")

Review a commit:

review_workspace(scope: "commit", commit: "abc123", cwd: "/path/to/repo")

security_audit

Run a security-focused review:

security_audit(instructions: "Focus on command execution and path traversal", cwd: "/path/to/repo")

Compatibility tools

run_antigravity and run_codex remain available for existing Claude rules, but they are intentionally narrower now.

run_antigravity

Query any model available in Antigravity CLI:

run_antigravity(promptContext: "Analyze this architecture", model: "gemini-3.1-pro")

List available models:

run_antigravity(subcommand: "models")

Run safe subcommands:

run_antigravity(subcommand: "help")
run_antigravity(subcommand: "changelog")

Allowed subcommands are models, help, and changelog. Plugin/install/update operations are blocked by design.

run_codex

Review uncommitted changes:

run_codex(action: "review", flags: ["--uncommitted"], cwd: "/path/to/repo")

Review with a specific prompt:

run_codex(action: "review", prompt: "Focus on security vulnerabilities")

Review against a branch:

run_codex(action: "review", flags: ["--base", "main"], cwd: "/path/to/repo")

Use a specific model:

run_codex(action: "review", prompt: "Audit this code", model: "gpt-5", cwd: "/path/to/repo")

Notes:

• prompt e flags são mutuamente exclusivos e agora são validados pelo MCP.

• Use cwd quando o comando depender do contexto de um repositório específico.

• action agora aceita apenas review.

• flags aceita apenas --uncommitted, --base, --commit, --title e --strict-config.

Error handling

Os tools retornam:

• comando executado

• cwd usado

• exit_code

• duration_ms

• stdout e stderr

• structuredContent com metadata da chamada

Se o processo sair com código diferente de zero, receber sinal ou estourar timeout, o resultado é marcado como erro no protocolo MCP.

Available Models

Via Antigravity

Via Codex

Codex models depend on your local Codex CLI configuration. Pass the model explicitly when needed:

review_workspace(scope: "uncommitted", model: "gpt-5", cwd: "/path/to/repo")
security_audit(model: "gpt-5", cwd: "/path/to/repo")

Making your AI agent orchestrate automatically

Add orchestration rules to your global ~/.claude/CLAUDE.md to make Claude Code call these tools proactively. See ORCHESTRATION.md for a full guide with triggers and patterns.

License

MIT