claude-bridges

MCP servers that let Claude Code delegate to other coding CLIs — cross-model second opinions, structured reviews, and a multi-engine consultation panel.

Most people use AI to autocomplete or to ask one model one question. I use multiple models as a structured thinking system with checks and balances: debate, execute, review, cross-check, iterate.

The Thinking Loop

flowchart TD
    A["🧠 I think / identify problem"] --> B["Claude Opus 4.6 @ claude.ai<br/>(debates with me, holds personal memory)"]
    B --> C["Claude Code: Fable refines plan<br/>(optional, costly)"]
    C --> D["Claude Code: Sonnet 5 executes<br/>(Opus 4.8 advisor) → HANDOFF.md"]
    D --> E["Opus 4.6 reviews with me"]
    E --> F{"Cross-model review<br/>(consult-bridge)"}
    F --> G["Gemini (agy-bridge)"]
    F --> H["GitHub Copilot CLI"]
    F --> I["OpenAI Codex CLI"]
    F -.-> J["Grok / others<br/>(planned, not yet wired)"]
    G & H & I --> K["Synthesize + next iteration"]
    K --> A

    style A fill:#2d3748,color:#fff
    style F fill:#4a5568,color:#fff
    style J fill:#718096,color:#a0aec0,stroke-dasharray: 5 5

This isn't "ask ChatGPT." Each model has a role, a sandbox, and a review gate. No single model's output ships unchecked.

What's Actually Wired

The consult tool currently fans out to three engines:

Engine	Wraps	Tier
copilot	GitHub Copilot CLI (copilot -p)	Free (GitHub)
codex	OpenAI Codex CLI (codex exec)	Free (ChatGPT)
agy	Antigravity / Gemini CLI	Paid (Gemini Pro/Flash)

Grok and others are in the aspirational loop but don't have bridges yet. The system is designed to add engines — each is ~30 lines in lib/engines.js.

Architecture

claude-bridges/
  package.json            shared deps (@modelcontextprotocol/sdk, zod)
  node_modules/           one install serves all bridges
  lib/
    common.js             shared MCP boilerplate, CLI runner, exe resolution
    engines.js            slim engine runners for consult-bridge's chain
  copilot-bridge/
    index.js              standalone MCP server → copilot_exec tool
  codex-bridge/
    index.js              standalone MCP server → codex_exec tool
  consult-bridge/
    index.js              aggregator MCP server → consult tool
  agy-bridge-vendored/    vendored copy of the agy-bridge (Gemini)

Three independent MCP servers, each usable standalone or through the consult aggregator:

Bridge	Tool	Default Posture
copilot-bridge	copilot_exec	Restrictive: no file writes, only read/search/git-gh shell auto-approved
codex-bridge	codex_exec	Sandboxed read-only
consult-bridge	consult	Aggregator: runs engines in parallel (mode: all) or sequential fallback (mode: first)

consult-bridge deliberately does NOT reuse the individual bridges' code paths — lib/engines.js duplicates ~30 lines of arg-building so the tested single bridges carry zero refactor risk.

Tools

consult (the star)

Consult a panel of external models. Two modes:

• all (default) — runs every engine in parallel, returns all successful answers labeled by engine. Use for cross-model second opinions where perspective diversity matters.
• first — sequential fallback chain, first success wins. Cheaper, use for routine questions or tight quotas.

consult({
  prompt: "Review this auth middleware for timing attacks",
  mode: "all",           // "all" | "first"
  order: ["copilot", "codex", "agy"]  // engine set/chain order
})

Engine order configurable via order arg or CONSULT_ORDER env (default copilot,codex,agy). A failed engine (quota, auth, timeout, empty output, nonzero exit) fails over to the next; the trail is appended to the result.

copilot_exec

Delegate to GitHub Copilot CLI, headless and non-interactive. Read-only by default — can read and search files under cwd but cannot modify them.

codex_exec

Delegate to OpenAI Codex CLI via codex exec. Sandboxed read-only by default. Pass sandbox: "workspace-write" only when you actually intend Codex to edit files.

Install

cd ~/claude-bridges
npm install

A single npm install at the root serves all servers — Node resolves node_modules by walking up from each index.js.

Register (user scope)

claude mcp add-json copilot-bridge  '{"command":"node","args":["C:/Users/YOU/claude-bridges/copilot-bridge/index.js"],"timeout":600000}' -s user
claude mcp add-json codex-bridge    '{"command":"node","args":["C:/Users/YOU/claude-bridges/codex-bridge/index.js"],"timeout":600000}' -s user
claude mcp add-json consult-bridge  '{"command":"node","args":["C:/Users/YOU/claude-bridges/consult-bridge/index.js"],"timeout":600000}' -s user

Replace C:/Users/YOU with your actual home directory. All three CLIs must be authenticated first:

copilot login    # GitHub Copilot CLI
codex login      # OpenAI Codex CLI
# agy: see agy-bridge docs for Gemini auth

Configuration (env vars)

Common

Variable	Default	Description
*_TIMEOUT	600	Per-engine timeout in seconds
*_MAX_OUTPUT_CHARS	50000	Truncation cap for output
*_MODEL	(engine default)	Override the model
*_BIN	(auto-resolved)	Override the CLI executable path

copilot-bridge

Variable	Default	Description
COPILOT_BRIDGE_PERM_ARGS	(restrictive defaults)	Replace permission flags wholesale. JSON array or whitespace-separated string. Deny rules always beat allow rules in Copilot.

codex-bridge

Variable	Default	Description
CODEX_BRIDGE_SANDBOX	read-only	read-only, workspace-write, or danger-full-access. Also overridable per call via the sandbox argument.

consult-bridge

Variable	Default	Description
CONSULT_TIMEOUT	300	Per-engine timeout in seconds
CONSULT_MAX_OUTPUT_CHARS	50000	Total output truncation cap
CONSULT_PER_ENGINE_CHARS	20000	Per-engine truncation in all mode
CONSULT_ORDER	copilot,codex,agy	Default engine order
CONSULT_MODE	all	Default mode (all or first)

Why This Exists

I've been building things since junior high — first Arduino sketches, now PCBs and trading systems. AI is part of my toolkit and my learning loop: I think with Opus 4.6 (planner/debater by nature), execute with Sonnet 5 + Opus 4.8 (strict prompt-followers, built to ship), and cross-check across Gemini, Copilot, and Codex before anything lands.

Each model has a job that matches how it was trained:

• Think — Opus 4.6 @ claude.ai holds my personal memory, debates approaches, and stress-tests plans before I commit. Its training leans toward reasoning and nuance — exactly what planning needs.
• Execute — Sonnet 5 writes the code; Opus 4.8 reviews as advisor. Both are trained for strict prompt-following and precise output — they ship, not philosophize.
• Cross-check — consult fans out to Copilot, Codex, and Gemini in parallel. Different training data, different blind spots, independent verdicts. No single model gets the last word.
• Iterate — everything feeds back to me. I synthesize, decide, and kick off the next cycle. The human stays in the loop at every gate.

Beyond model selection, each delegation gets a role prompt — "As a Security auditor…", "As a Frontend engineer…", "As a Karpathy-style code reviewer…" — so the model responds from a specific expertise frame, not as a generic assistant. I maintain a full role table (20+ roles across engineering, product, content, and research) that maps each role to the right model tier and tool.

The bridges are the plumbing that makes cross-checking programmatic — not manual copy-paste between chat windows.