agy-gemini-mcp

Русская версия

An MCP server that lets Claude Code hand work to Gemini through the Antigravity CLI (agy), using a Google AI Pro subscription instead of an API key. Claude is the boss: it writes the task, reads what Gemini produced, and sends corrections. Gemini does the heavy lifting (writing code, drawing images), so your Claude limits go mostly to reviewing.

This project is not affiliated with Google or Anthropic. Read the "Risks" section before you use it.

Claude writes about 4 to 5 times less on big coding tasks (our estimate)

One large task we ran end to end: a small expression language (parser, evaluator, CLI, 71 tests).

Claude also read about 29 KB (around 7,000 input tokens) of the code in review. Gemini spent roughly 0.7M tokens of the Google quota and 13 + 7.5 minutes of waiting on that task (first version, then four fixes after Claude's review found real bugs).

What this number is not: a measurement of your plan's usage limit. It counts only the text Claude writes, from file sizes at about 3.5 characters per token. Every tool call also re-reads the conversation, and thinking adds cost on both sides, so the saving in total usage is smaller than the output ratio and depends on your plan. We did not measure against the usage counter. For small tasks there is no saving at all, and the gemini-code skill makes Claude do those itself.

Related MCP server: agent-delegation-mcp

Why Antigravity CLI and not Gemini CLI

Google stopped serving Gemini CLI to individual accounts (including Google AI Pro) on 18 June 2026 (discussion, report). The replacement is the Antigravity CLI, binary name agy. It signs in with your Google account and has a headless mode (agy -p "..."), which is what this server drives.

Setup

1. Install Antigravity CLI from antigravity.google.

2. Use a Google account that has a Google AI Pro subscription.

3. Run agy once in a terminal and sign in with that account.

Regions where Antigravity is not available

Antigravity is not offered in every country. We saw two separate layers: a local check inside agy ("not currently available in your location") and a server-side check by IP address, where Google answers User location is not supported for the API use (HTTP 400). In our test, removing the local check was not enough: the server-side answer stayed the same until the network path to Google was fixed. Other users report that Google traffic excluded from a VPN tunnel, DNS servers of the local provider, and VPN servers that Google treats as data-center addresses cause this (Meduza, Habr discussion); these are anecdotes, not guarantees. You can check which country Google sees from the terminal that runs agy with curl https://ipinfo.io/country.

This project does not provide, link or endorse tools or services for getting around regional limits. Whether and how to use any of them is your decision and your risk.

Install the server

git clone https://github.com/velime/agy-gemini-mcp
cd agy-gemini-mcp
npm install
claude mcp add --scope local agy -- node /absolute/path/to/agy-gemini-mcp/server.mjs

--scope local enables it only for the current project. Use --scope user to enable it everywhere. Restart Claude Code afterwards. To check that everything works end to end (this makes two small real requests to Gemini):

node selftest.mjs

Optional: copy skills/gemini-code and skills/gemini-image-loop into .claude/skills/ of your project (or ~/.claude/skills/) to get the coding and image workflows described below.

Settings (environment variables): AGY_PATH (path to the agy binary, found automatically on Windows), AGY_WORK_ROOT (the only folder Gemini may work in, default ~/gemini-work).

Tested on: Windows 11, Node.js 24, Python 3.13, Antigravity CLI 1.4.2 and 1.4.3, Claude Code in the Claude desktop app. Linux and macOS are not tested: the server falls back to an agy found on PATH there, and everything else uses portable paths, but we have not run it. The live view (tools/watch_agy.py) reads agy's internal step log, which is undocumented and may change in an agy update.

How it works inside

Three tools:

• agy_run(prompt, workdir, conversation_id?, model?, effort?, mode?, timeout_min?) starts a Gemini job and returns a job_id immediately.

• agy_result(job_id, wait_s?) waits up to 45 seconds and returns the status, Gemini's answer, conversation_id and token usage, or state: running if it is still working.

• agy_cancel(job_id) kills a job.

Details that matter:

• Each job runs agy --output-format json --dangerously-skip-permissions -p "<prompt>" with the work folder as the current directory. Jobs are asynchronous because a Gemini job takes minutes.

• agy's output goes to a file, not to a pipe. A third-party report says agy print mode on Windows can return nothing when stdout is a pipe (gist), and quota errors can look like success. The server treats "exit code 0 but no JSON" as an error.

• Pass the conversation_id from a result into the next agy_run and Gemini continues the same session. This is how corrections work: Gemini remembers what it did.

• Prompts longer than 6000 characters are written to TASK.md in the work folder, because of the Windows command-line limit.

• The work folder must be inside AGY_WORK_ROOT. This is a guard against mistakes, not a sandbox: agy runs with auto-approve, so Gemini can in principle run commands that touch other places.

• Jobs are kept in memory only; restarting the server forgets running jobs.

Live view of what Gemini is doing: tools/watch_agy.py reads agy's own step log read-only and prints one line per step as it happens (commands it runs, files it writes, sub-agent calls, API errors):

python tools/watch_agy.py

In the Claude desktop app the skills open it for you in a Terminal tab called "Gemini live", so you see what Gemini is doing without leaving Claude. It follows the most recently active conversation (--conversation ID to pick one, --from-start to also print earlier steps).

Journal: the skills make Claude keep a journal of delegated work. After each finished task Claude appends a short entry to JOURNAL.md in the work root: what was asked, how many Gemini jobs, seconds and tokens, the result, what the review found, and any problem (API stall, region or quota error). It is plain Markdown that you can read, and it is the data for judging whether delegating paid off, for example before replacing the estimate at the top of this page with a measurement.

Using Gemini for code, with Claude as the reviewer

The skill gemini-code does this and sizes the work first: tiny edits Claude does itself (delegating would cost more than the edit), small tasks go out as a short direct brief on effort: low (seconds to 2 minutes), medium and large tasks get a full brief, more time and a heads-up to the user (around 15 minutes for a 500-line job). Without the skill, give Claude an instruction like this (or put it in CLAUDE.md):

For big coding tasks, do not write the code yourself. Delegate with the agy tools: write a brief with concrete acceptance criteria, run it with agy_run in a folder under the work root, read the result and the files Gemini produced, run the tests, and send exact corrections into the same conversation with conversation_id. Stop after 5 rounds and report.

When it is worth it: large, write-heavy work (new modules, many files, boilerplate, tests). Claude spends tokens on the brief and the review instead of on generating hundreds of lines (see the estimate at the top: about 4 to 5 times less text written by Claude, and a smaller saving in total usage). When it is not: small fixes in one or two files. Writing the brief, waiting minutes and reading the answer costs more than just doing it.

Costs on the Gemini side, measured on our runs: a large first job (a 550-line tool with tests) took 14 minutes and about 420k tokens; a small, narrowly worded job (a 54-line tool with tests) took 27 seconds and 57k tokens; a follow-up fix in the same conversation took 40 seconds to 2 minutes. Your Google quota is the new bottleneck, so keep tasks focused.

Why jobs can be slow, found by reading agy's own step log (~/.gemini/antigravity-cli/conversations):

• Exploration and scratch scripts. If the brief invites it ("match the real HTML", "be exact"), the agent runs dozens of experiments. For the 550-line job, 31 Python scripts and 35 file reads and writes. Ask for the work directly: "write the files directly, do not explore, no scratch files, run the tests once".

• Long generation. Writing big files with a thinking model is slow (33k thinking tokens in that job). effort: low removes most of the thinking.

• API stalls. In that job 300 of the 966 seconds were one "stream idle timeout (5m0s)" followed by an automatic retry. The job still finishes, so a status of ERROR with a complete answer is possible. The server returns the cause in agy_error. Nothing on our side can prevent this; it is the service. Earlier jobs showed a similar loss to an INTERNAL (code 500) error.

Gemini works in a folder under the work root. To let it edit a real project, either copy the project there and merge the result after review, or set AGY_WORK_ROOT to the folder that holds your projects (riskier, because of auto-approve).

Images: Gemini draws, Claude directs

Gemini generates images inside agy through its built-in image generator, with no API key (we checked: a red apple on white, a mug with text). It is weak at following a brief:

• text inside the image is often wrong or garbled;

• counts of objects and "do not draw X" are often ignored;

• every correction redraws the whole picture. Asked only to recolor a mug from blue to red, it also changed the background and the room.

So Claude acts as an art director (skill gemini-image-loop). It turns your request, in any language, into a checklist and an English prompt that Gemini can follow: the meaning stays yours, but details are made concrete (subject, framing, lighting, background, what must not appear). It sends the prompt to Gemini, looks at the PNG itself, and checks it against the checklist. Where the image is wrong, Claude rewrites the prompt at exactly those places (more detail, clearer wording, everything else unchanged) and sends the full revised prompt again. Up to 5 iterations, and it stops to ask you after 3 failed tries in a row. When Claude judges the image correct, it shows it to you and asks whether it is right. Each look at an image costs some Claude usage, hence the cap.

Speed: keep prompts short. We measured the same photo edit twice. A long prompt that demanded that everything else stay "exactly" the same made Gemini's agent run 42 Python/Pillow steps (palette extraction, array diffs, compositing) and take 9+ minutes. A short prompt that said "use only your image generation tool, do not use Python or any image library" took 79 seconds and about a quarter of the tokens. The skill is written this way.

The same loop edits an existing photo. Gemini only sees files in its work folder, so Claude copies your photo there as input.png (a photo you attach in the Claude chat is saved by the client to a file whose path Claude sees; a photo from a folder is copied from there; the original is never touched), looks at it, and writes a prompt that says what to keep and what to change. We checked it: given an apple photo, Gemini added a ribbon to the stem and left the apple, leaf, background, framing and lighting as they were.

Tournament skill (optional)

If you want Gemini competitors to attack each other's answers and pick the best one, see the separate repository gemini-arena-skill. It is expensive in Gemini quota, so it is a separate, optional install.

Risks

• Terms of service. Using Antigravity from a region where it is not offered, or with a subscription you did not buy from Google, may violate Google's terms. The account can be limited or banned. Use an account you can afford to lose.

• --dangerously-skip-permissions means Gemini can run commands without asking. Keep AGY_WORK_ROOT away from anything you care about.

• Quota: heavy use can exhaust your daily Google limit. The server reports quota errors instead of retrying.

• Everything here is provided as is, without warranty.

License

MIT, see LICENSE.