Claude Code × Antigravity + Codex + Copilot + Cursor — MCP Bridge

Drive four external coding CLIs — Google's Antigravity (Gemini 3.5 Flash), OpenAI Codex, the GitHub Copilot CLI, and Cursor — as sub-agents inside Claude Code. Text answers, image generation, real repo work, and parallel swarms, on quota you already pay for.

One MCP server, four backends. It exposes Google Antigravity, OpenAI Codex, the GitHub Copilot CLI, and Cursor to Claude Code as clean MCP tools so you can delegate work to a different model family mid-task — without leaving your terminal, and on the subscriptions you already have. Each backend is independent: install one, two, three, or all four.

• 🛰️ Antigravity (agy, Gemini 3.5 Flash High). Fast, cheap tool-calling — and the only backend with an image model. Its headless print mode (agy -p) historically had a stdout bug: it wrote the answer to the controlling terminal instead of its stdout, so anything capturing stdout got nothing (and, under a TUI, agy's text leaked into the host's prompt). agy 1.0.15 fixed this on Windows — -p now writes the clean answer to stdout — so the bridge prefers stdout and falls back to reading agy's own transcript files only when stdout is empty (older agy, non-Windows, or --sandbox runs). It still detaches agy from the terminal so older versions can't leak.
• 🤖 Codex (codex exec, OpenAI). A strong reasoner for real code/repo work. It writes its final message straight to a file the bridge asks for (no scraping), supports model selection, and has a real, enforced sandbox.
• 🐙 Copilot (copilot -p, GitHub). GitHub's agentic coder. Stdout-native like Codex (-s prints just the answer), with model selection (--model), a best-effort tool/path permission knob, and a deterministic resume mechanism (the bridge sets each session's UUID itself).
• ✳️ Cursor (cursor-agent -p, Cursor). Cursor's agentic coder, with the widest model menu — GPT, Claude, Grok, and Composer via --model (validated against cursor-agent models). Stdout-native like Codex/Copilot (--output-format text prints just the answer), an agent-enforced sandbox (read-only via --mode ask), and a deterministic resume mechanism (the bridge mints each chat's id itself via create-chat). No image model.

All four share the same niceties: a *_continue to resume a thread, a live "watch" window to see the agent work, a unified agent_swarm that runs many tasks in parallel across all backends at once, and *_status diagnostics that spend no quota.

[!WARNING] This runs unsandboxed code with your privileges. agy -p auto-executes its tools (read/write files, run shell commands, reach the network) with no usable approval gate — its --sandbox blocks only shell commands, leaving file writes and network egress wide open. codex exec also runs autonomously, but its sandbox flag (default read-only) is a real, enforced boundary. copilot -p runs headless with --allow-all-tools; its sandbox maps to best-effort tool/path permissions (read-only denies the local write/shell tools) — safer than agy, but not an OS sandbox like Codex's. cursor-agent -p runs headless with --trust (and --force for writes); its sandbox is agent-enforced (read-only = --mode ask, which makes the write/shell tools unavailable) — best-effort like Copilot, not an OS sandbox. In all four cases the workspace argument is a starting context, not a security boundary. Only use these with trusted prompts on trusted content; for real isolation, run the bridge inside a container or VM. Full details →

Why you'd want this

The four backends at a glance

The bridge normalizes all four CLIs into the same shape, but they differ where it matters. Pick per task:

How it works

All four backends run headless and one-shot per call; the bridge's job is to get a clean answer out of each and hand it to Claude Code as a plain string.

flowchart LR
    A([Claude Code]) -- "MCP tool call" --> B["bridge<br/>(server.py)"]
    B -- "antigravity_*" --> C[agy -p]
    B -- "codex_*" --> D[codex exec]
    B -- "copilot_*" --> E[copilot -p]
    B -- "cursor_*" --> F[cursor-agent -p]
    C -- "stdout (1.0.15+)<br/>or transcript.jsonl / .db" --> B
    D -- "output-last-message file" --> B
    E -- "stdout (-s silent)" --> B
    F -- "stdout (--output-format text)" --> B
    B -- "plain text" --> A

Antigravity. On agy 1.0.15+ (Windows), agy -p writes its clean final answer straight to stdout, and the bridge returns that directly. On older agy — or non-Windows, or a --sandbox run — stdout stays empty and the bridge falls back to agy's own transcript at:

~/.gemini/antigravity-cli/brain/<conv-id>/.system_generated/logs/transcript.jsonl

For the fallback it locates the conversation via cache/last_conversations.json (falling back to the newest brain/ directory touched since launch), streams the transcript, and returns the final source=MODEL, status=DONE, type=PLANNER_RESPONSE entry — the answer, minus the intermediate tool-calling steps (or the SQLite .db agy dual-writes, when no JSONL exists). Either way, antigravity_continue pins the workspace's exact conversation id via --conversation, so it never resumes the wrong thread.

Codex. codex exec is well-behaved: the bridge passes -o/--output-last-message <file> and codex writes its final message straight there — no scraping. Continue works by capturing the session id from codex's own rollout files (~/.codex/sessions/.../rollout-*.jsonl) and resuming with codex exec resume <id>, falling back to the newest on-disk session for that cwd after a server restart.

Copilot. copilot -p "<prompt>" -s runs a prompt non-interactively and prints the clean final answer to stdout — the bridge reads it there, no scraping. It runs headless with --allow-all-tools --no-ask-user --no-auto-update (so it never blocks on a prompt), and disables copilot's flaky builtin GitHub-API MCP by default for predictable latency (COPILOT_GITHUB_MCP=1 re-enables it). Continue is deterministic: copilot's --session-id <uuid> both sets a new session's id and resumes an existing one, so the bridge generates the UUID itself, pins it to the workspace, and resumes that exact session — falling back after a restart to the newest on-disk session (~/.copilot/session-state/<id>/workspace.yaml) whose recorded cwd matches.

Cursor. cursor-agent -p --output-format text --trust "<prompt>" runs a prompt non-interactively and writes the clean final answer straight to stdout — the bridge reads it there, no scraping (--trust trusts the workspace so it never blocks on a prompt). Continue is deterministic and race-free: cursor-agent create-chat mints a fresh chat and prints its id, so the bridge mints the id itself, pins it to the workspace, and resumes that exact chat with -p --resume <chatId> — no rollout-scraping. After a restart it falls back to the newest on-disk chat under ~/.cursor/chats/<md5(workspace)>/<chat-id>/ whose meta.json cwd matches (the chat-dir hash is itself md5 of the workspace path).

Set up in 60 seconds

Prerequisites — install whichever backend(s) you want, and sign in once each:

• Antigravity: install agy and sign in to Antigravity once (via the IDE or agy -i).
• Codex: install codex and run codex login once (ChatGPT account or API key).
• Copilot: install copilot (npm i -g @github/copilot, or winget install GitHub.Copilot) and run copilot then /login once (or set a COPILOT_GITHUB_TOKEN/GH_TOKEN env var).
• Cursor: install cursor-agent (curl https://cursor.com/install -fsSL | bash) and run cursor-agent login once (or set a CURSOR_API_KEY env var).

You don't need all four — the tools for a missing CLI simply report "not found" via their *_status tool.

Recommended — no clone, you control updates

With uv installed, register the bridge straight from PyPI under mcpServers in ~/.claude.json — no path to hardcode, no git pull to remember:

"agent-intern": {
  "command": "uvx",
  "args": ["agent-intern"]
}

uvx pins to the version it first caches and does not auto-upgrade, so you never run an update you didn't choose — important, since the bridge runs unsandboxed code: a surprise (or compromised) release can't execute until you opt in. When the startup check warns that a newer release is out, upgrade deliberately and restart Claude Code:

uvx agent-intern@latest      # fetch + run the newest release (refreshes uv's cache)

[!TIP] Prefer hands-off auto-updates? Put "args": ["agent-intern@latest"] in the config instead — every launch runs the newest release. Convenient, but it pulls new code without asking each time.

From source

Clone it instead if you want to hack on the bridge or pin a local copy:

git clone https://github.com/SinanTufekci/agent-intern.git
cd agent-intern
pip install fastmcp
python test_smoke.py        # 4 real round-trips (ask, continue, image, swarm) — prints four PASS lines

[!NOTE] The smoke test costs a tiny bit of quota and takes ~30–60 s. It exercises the Antigravity path.

Then point Claude Code at the absolute path to server.py under mcpServers in ~/.claude.json:

"agent-intern": {
  "command": "python",
  "args": ["C:\\path\\to\\server.py"]
}

"agent-intern": {
  "command": "python3",
  "args": ["/path/to/server.py"]
}

Restart Claude Code. Fifteen tools appear, each prefixed mcp__agent-intern__:

• Antigravity (5): antigravity_ask, antigravity_continue, antigravity_image, antigravity_image_swarm, antigravity_status
• Codex (3): codex_ask, codex_continue, codex_status
• Copilot (3): copilot_ask, copilot_continue, copilot_status
• Cursor (3): cursor_ask, cursor_continue, cursor_status
• Shared (1): agent_swarm — fans a list of tasks out across all four backends in one run

The single-prompt tools — Antigravity, Codex, Copilot, and Cursor — take a watch=true flag for the live browser view (Watch mode).

[!NOTE] Your client learns how to use the bridge on its own. The server ships MCP instructions — a short routing guide (when to reach for each tool, which backend to pick, and to pass workspace so the sub-agent has repo context) that a client like Claude Code injects into the model's context on connect, as an "MCP Server Instructions" block. So the host model knows how and when to drive these tools without you explaining them — you can just ask for the result.

"Use antigravity_ask to summarize the README of this repo in three bullets." → Claude routes the prompt through the bridge, agy reads the file under the workspace root, and the answer comes back as a plain string. Swap in codex_ask, copilot_ask, or cursor_ask to have GPT, Copilot, or Cursor do the same.

Tools

🛰️ Antigravity

🤖 Codex

🐙 Copilot

✳️ Cursor

🐝 Shared

workspace defaults to the MCP server's current working directory. Point it at a real project dir for context-aware answers — every backend gives the model access to files under that root (Codex, Copilot, and Cursor honoring their sandbox).

antigravity_image forces agy to save to an explicit absolute path — without one, agy falls back to its own scratch dir (~/.gemini/antigravity-cli/scratch/). It then corrects the file extension to match the real bytes: agy's image model picks the format itself (JPEG for photo-like images, PNG for flat graphics), so a requested out.png may come back as out.jpg. The returned path always reflects the true format.

🤖 Codex bridge — the well-behaved sibling

codex exec writes its final message to a file the bridge asks for via -o/--output-last-message, so the answer comes back without any scraping (where agy needed a transcript workaround before 1.0.15 fixed its stdout). Three things make Codex worth reaching for over Antigravity:

• Real sandbox. sandbox accepts read-only (default — reads and answers, writes nothing), workspace-write (may edit files under the workspace), or danger-full-access (no sandbox — avoid). Unlike agy's no-op --sandbox, codex's -s actually enforces this. codex exec has no interactive approval gate, so this flag is your safety boundary — opt into write access deliberately.
• Model selection works. model maps to codex's -m. (agy's --model works in print mode too as of 1.0.16; all four backends now expose the same model knob.)
• Stronger reasoning. Codex is a coding agent, not an image model — there's no codex_image. Its strength is reasoning and real code/repo work; hand it the jobs that need a heavier model.

Auth. Uses your existing Codex login (ChatGPT account or API key). Run codex login once; check with codex_status. No new keys for the bridge to manage.

[!WARNING] codex exec runs the model as an autonomous agent with no interactive approval gate. The sandbox flag (default read-only) is the real boundary, but workspace-write / danger-full-access let it modify files — and a swarm runs N agents at once. Only use it with trusted prompts on trusted content.

🐙 Copilot bridge — GitHub's agentic coder

The GitHub Copilot CLI (copilot, from @github/copilot) is stdout-native like Codex: copilot -p "<prompt>" -s runs a prompt non-interactively and prints just the final answer to stdout, so the bridge reads it there — no scraping. What makes it worth reaching for:

• Model selection. model maps to copilot's --model (e.g. gpt-5.3-codex, claude-sonnet-4.6, or auto). An unavailable model errors immediately with a clear message.
• Deterministic, race-free continue. copilot's --session-id <uuid> both sets a new session's id and resumes an existing one, so the bridge generates the UUID itself and pins it to the workspace — no rollout-scraping. After a restart it falls back to the newest on-disk session (~/.copilot/session-state/<id>/workspace.yaml) whose recorded cwd matches.
• Fast by default. Runs with --allow-all-tools --no-ask-user --no-auto-update, and disables copilot's builtin GitHub-API MCP (--disable-builtin-mcps) because its flaky HTTP connect can stall a call up to ~60 s. Set COPILOT_GITHUB_MCP=1 to keep it (for Copilot's issue/PR/repo tools).

Sandbox is best-effort, not enforced. Unlike Codex's OS sandbox, copilot's boundary is tool/path permissions. The sandbox knob maps to copilot flags for a uniform cross-backend field:

• read-only (default) — auto-approves tools so it runs headless, then denies the local write and shell tools (--deny-tool). Best-effort: it is not an OS sandbox, and network/MCP tools can still act. For a hard read-only boundary, use codex_ask instead.
• workspace-write — writes allowed, but file access stays confined to the workspace (no --allow-all-paths).
• danger-full-access — --allow-all (tools + all paths + all URLs). Avoid.

Auth. Uses your existing Copilot login — run copilot then /login once (stored in the OS credential store), or set COPILOT_GITHUB_TOKEN/GH_TOKEN/GITHUB_TOKEN for headless use. Check with copilot_status. If copilot isn't on PATH (the winget install can land off a stale PATH), set COPILOT_BIN to its full path — e.g. %LOCALAPPDATA%\Microsoft\WinGet\Packages\GitHub.Copilot_*\copilot.exe.

[!WARNING] copilot -p runs the model as an autonomous agent with --allow-all-tools (required to run headless). Its sandbox is best-effort tool/path permissions, not an OS sandbox — safer than agy, weaker than Codex's read-only. Only use it with trusted prompts on trusted content.

✳️ Cursor bridge — the widest model menu

Cursor's agent CLI (cursor-agent, from cursor.com/cli) is stdout-native like Codex and Copilot: cursor-agent -p --output-format text --trust "<prompt>" runs a prompt non-interactively and writes just the final answer to stdout, so the bridge reads it there — no scraping (--trust trusts the workspace so it won't block on a prompt). What makes it worth reaching for:

• The widest model menu. model maps to cursor's --model (e.g. gpt-5.2, sonnet-4-thinking, auto, and parameterized ids like claude-opus-4-8[context=1m]) — GPT, Claude, Grok, and Composer in one place. The bridge validates the label against cursor-agent models and rejects a typo up front (like agy). Omit model to use your Cursor account default.
• Deterministic, race-free continue. cursor-agent create-chat mints a fresh chat and prints its id, and -p --resume <chatId> resumes that exact chat — so the bridge mints the id itself, pins it to the workspace, and resumes deterministically (no rollout-scraping, same idea as Copilot's self-set session id). After a restart it falls back to the newest on-disk chat under ~/.cursor/chats/<md5(workspace)>/<chat-id>/ whose meta.json cwd matches (the chat-dir hash is itself md5 of the workspace path).

Sandbox is agent-enforced, not an OS sandbox. Like Copilot, cursor's boundary is which tools the agent can reach, not an OS jail. The sandbox knob maps to cursor's mode/force flags for a uniform cross-backend field:

• read-only (default) — --mode ask: the write and shell tools are unavailable, so cursor analyzes and answers but makes no edits (verified: it refuses to write files). Agent-enforced and best-effort — it is not an OS sandbox. For a hard read-only boundary, use codex_ask instead.
• workspace-write — --force: edits and commands allowed, file access rooted at --workspace.
• danger-full-access — --force --sandbox disabled (OS sandbox off). Avoid.

(Cursor also exposes an OS-level --sandbox enabled/disabled; the bridge drives the uniform field via mode/force.)

Auth. Uses your existing Cursor login — run cursor-agent login once (OS credential store), or set CURSOR_API_KEY for headless use. Check with cursor_status. If cursor-agent isn't reliably on PATH (the installer drops a cursor-agent.CMD shim a bare name can't launch on Windows), set CURSOR_BIN to its full path — mirrors the AGY_BIN/CODEX_BIN/COPILOT_BIN overrides.

[!WARNING] cursor-agent -p runs the model as an autonomous agent with --trust (and --force when writes are allowed). Its sandbox is agent-enforced (read-only makes the write/shell tools unavailable), not an OS sandbox — safer than agy, weaker than Codex's read-only. Only use it with trusted prompts on trusted content.

👁️ Watch mode — Agent Intern (experimental)

Pass watch=true to any single-prompt tool — antigravity_ask, antigravity_continue, antigravity_image, codex_ask, codex_continue, copilot_ask, copilot_continue, cursor_ask, or cursor_continue — to watch the agent work live in a little chat-style browser window called Agent Intern. The agent still runs headless; alongside it the bridge serves a tiny page on 127.0.0.1 and opens it in a small, chromeless app window that renders the exchange as a conversation: your prompt shows as a chat bubble, the agent's live steps stream in a collapsible "thinking" trace — its planner narration (▸), the real commands it runs ($), and completions (✓), read live (from agy's transcript, or codex's / copilot's JSON event stream, or cursor's --output-format stream-json) — and the final answer arrives as a Markdown card (and, for antigravity_image with watch=true, the generated image shown inline). A *_continue run opens with the prior turns of the conversation shown as history, so it reads as one ongoing thread rather than a blank new window. (A watched cursor_continue is the exception — Cursor stores its transcript in an opaque SQLite blob, so its window opens without visible prior-turn history.)

• Cross-platform & best-effort. Prefers a Chromium browser (--app mode) for the windowed look; falls back to a normal browser window. If nothing can open, the run still completes and returns normally.
• Window size. Set AGY_WATCH_WINDOW_SIZE (e.g. AGY_WATCH_WINDOW_SIZE=480,700) to resize the window; default is 560,760. Press Enter / Esc in the window to close it.
• One window, reused — but concurrent runs stay separate. Repeated sequential watch calls reuse the already-open window instead of stacking a new one (the open page resets itself for the new run; the swarm dashboard rebuilds for the new fan-out). A run that starts while another watched run is still working gets its own window instead — so two concurrent single-worker runs (e.g. a codex_ask and a copilot_ask at once) each stream into their own view and never clobber each other. If you closed the window, the next run opens a fresh one. Set AGY_WATCH_ALWAYS_NEW=1 to force a new window every time.
• Chat layout & history. Prompts render as chat bubbles (labelled CLAUDE, since the MCP client writes them) — long ones clamp to a few lines with a show more / show less toggle — and answers as Markdown cards tagged with the backend (AGY / CODEX / COPILOT / CURSOR). A *_continue run seeds the window with the conversation's prior turns, read from each backend's own session store (agy's transcript, codex's rollout, copilot's events.jsonl; Cursor's store is opaque, so a watched cursor_continue opens without visible history). The swarm's per-worker detail window uses the same chat design for its one task.
• Progress, keyboard & copy. Each panel shows a time progress bar (elapsed / timeout). The swarm dashboard adds an overall done/total bar and per-row time bars; use ↑/↓ to select a worker and ↵ to open its detail window. Answers render as Markdown with a copy button, and a "jump to latest" badge appears if you scroll up.
• Coarse, not token-level. The backends flush their step stream in chunks, so you get a handful of live steps, not character streaming. The returned value is identical to the non-watch call. Nothing is sent anywhere but your own machine.

🐝 Swarm — run agents in parallel

agent_swarm fans a list of tasks out to workers that run truly concurrently (capped at max_concurrency, default 4), then returns every worker's result in one block. Each task names its own backend, so a single swarm can mix Antigravity (Gemini), Codex, Copilot, and Cursor workers — hand the reasoning-heavy jobs to Codex, Copilot, or Cursor and the quick ones to Gemini, all at once. Good for independent sub-tasks: summarise N files, ask the same question about N repos, fix N bugs. (antigravity_image_swarm stays separate — it generates N images, and only agy has an image model.)

agent_swarm(tasks=[
  {"backend": "antigravity", "prompt": "Summarise src/auth.py in 2 bullets."},
  {"backend": "codex", "prompt": "Find and fix the failing test in tests/",
   "sandbox": "workspace-write", "workspace": "./repo"},
  {"backend": "copilot", "prompt": "Explain what src/api.py exposes.",
   "sandbox": "read-only", "workspace": "./repo"},
  {"backend": "cursor", "prompt": "Draft a docstring for src/utils.py.",
   "model": "auto", "workspace": "./repo"},
])

How it stays correct under concurrency. The single-agent agy tools serialize through a lock because agy rewrites last_conversations.json on every call, so concurrent runs sharing one state dir would race. The swarm sidesteps this: each agy worker runs with its own isolated HOME/USERPROFILE, so agy's brain/, cache/, and last_conversations.json never collide — no lock needed. Auth still works because agy reads it from the OS credential store, not from ~/.gemini (verified on agy 1.0.9). Codex, Copilot, and Cursor workers need no such isolation — each is a fresh one-shot (codex exec with its own -o file; copilot -p with its own self-set session id; cursor-agent -p with its own minted chat id). Each worker's cwd is its real workspace, so file access is unchanged. Measured ~2.8× speedup at 3 agy workers (the AI Pro backend does not serialize per-account); higher max_concurrency trades quota/rate-limit pressure for wall-clock.

• Per-task fields — backend (antigravity/codex/copilot/cursor) and prompt are required; workspace defaults to the server cwd; sandbox and model apply to Codex, Copilot, and Cursor (ignored for Antigravity). Swarm workers are one-shot — there is no *_continue for a swarm worker's session.
• Error isolation — a worker that fails is reported in place; the others still return.
• watch=true — opens a thin live Agent Swarm dashboard (one row per worker, with a backend badge, repo, prompt, and latest step). Click a row to pop that agent into its own window streaming its full step log.

[!WARNING] A swarm launches N unsandboxed agents at once — N× the prompt-injection "lethal trifecta" surface of a single call (see Security). Only use it with trusted prompts on trusted content. Codex workers honor their enforced sandbox; Copilot and Cursor workers honor their best-effort sandbox; Antigravity workers have no real boundary.

Model & auth

⚠️ Security

All four backends run the model as an autonomous agent. The difference is whether you get a real boundary: Codex enforces one, Copilot and Cursor offer best-effort ones, Antigravity offers none.

Antigravity — no usable boundary

agy -p executes its own tools — reading and writing files, running shell commands, reaching the network — with no approval gate. Through agy 1.1.2 that was simply how print mode worked, with no opt-out at all. As of 1.1.3 it is a choice the bridge makes: agy finally gates headless tool calls, and the bridge deliberately opts out with --dangerously-skip-permissions, because a gated -p can do no useful work (it soft-denies even a plain file read, and print mode has no way to prompt). The posture below is therefore unchanged — assume every call runs arbitrary