supergravity-mcp
The supergravity-mcp server automates the Google Antigravity desktop app via UI-driven automation (no credentials or internal APIs required), enabling you to:
delegate_to_antigravity: Send a task, question, or instruction to Antigravity and receive its reply. Optionally specify:A model: Gemini 3.5 Flash, Gemini 3.1 Pro, Claude Sonnet 4.6, Claude Opus 4.6, or GPT-OSS 120B (each with different cost/capability tradeoffs)
A timeout (default 2 minutes) to cap how long to wait for a response
get_antigravity_quota: Read remaining usage quota from Antigravity's Settings, showing two separate pools (Gemini vs. Claude+GPT) — useful for pre-checking availability or diagnosing a disabled send button.Run tasks in batch: Execute multiple independent tasks concurrently in separate Antigravity windows (requires macOS Accessibility permission).
Retrieve generated files: List files generated by Antigravity across all conversations (most recent first), with direct filesystem paths to avoid fetching through Antigravity's local server.
Allows delegation of tasks to the Google Antigravity desktop app via UI automation, enabling AI agents to interact with models like Gemini, Claude, and GPT through Antigravity's interface.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@supergravity-mcpask Antigravity to summarize the latest news"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
supergravity-mcp

An MCP server that lets Claude Code (or any MCP client) delegate tasks to the Google Antigravity desktop app — by driving its real, already-logged-in UI. No reverse engineering, no extracted credentials, no unofficial API calls.
Why this exists
Antigravity ships a CLI (agy) for scripting it headlessly — but on older
Intel Macs, that CLI isn't available even though the desktop app runs fine
(confirmed working on a 2015 MacBook Pro, macOS Monterey, Intel). This project
is the missing bridge for anyone in that situation: it automates the app the
same way a human would use it, so it works anywhere the app itself works.
Related MCP server: realhands
How it works
Antigravity is relaunched with Chrome's remote-debugging flag (
--remote-debugging-port), the same mechanism browser test tools like Playwright use to drive Electron apps.A script connects to that debug port and: types your task into the chat box (via a simulated paste event — Antigravity's input is a rich-text editor, not a plain textarea, so it needs a real paste, not just poking text into the DOM), clicks send, and reads back the reply once it's done streaming.
Everything happens through your own existing login. This tool never touches Antigravity's stored credentials, internal code, or private APIs.
Setup
Antigravity must be installed and already signed in. By default the tool
looks for it at /Applications/Antigravity.app or ~/Desktop/Antigravity.app;
set ANTIGRAVITY_APP_PATH if it lives somewhere else.
Easiest: install the plugin (MCP server + skill together)
claude plugin marketplace add presidentrice/supergravity-mcp
claude plugin install supergravity@supergravity-mcpThis gets you both the delegate_to_antigravity / get_antigravity_quota
tools and the supergravity:delegate skill, which teaches Claude how to
route natural requests ("use gemini", "ask antigravity", "check antigravity's
quota") to the right tool call and model.
Or: just the MCP server, no skill
claude mcp add supergravity -- npx -y supergravity-mcpWorks fine without the skill — Claude still has the tools, just less built-in guidance on picking a model or handling a quota-exhausted error.
Building from source
npm install
npm run buildThe tools
delegate_to_antigravity(task, model?, timeoutMs?)
task— what to ask Antigravity to do.model— optional. One of the models in Antigravity's own selector (seeAVAILABLE_MODELSinsrc/antigravity-client.ts).timeoutMs— optional, default 120000 (2 minutes).
If the task generates files (images, documents, etc.), the result also
includes a real filesystem path — Antigravity stores each conversation's
files at ~/.gemini/antigravity/brain/<conversation-id>/ on disk, confirmed
directly (not documented anywhere by Google). No need to fetch anything
through Antigravity's local server; just read the file.
delegate_to_antigravity_batch(tasks)
Runs several independent tasks at once, each in its own Antigravity window — genuinely concurrent, not queued. Confirmed with two simultaneous ~200-word generations that both completed correctly in the same ~16s window. Opens extra windows automatically as needed (macOS's "New Window" menu command via System Events, with a fallback for when zero windows are open).
Requires Accessibility permission for whatever process runs this (System
Settings > Privacy & Security > Accessibility) — macOS only. Don't run this
at the same time as a separate delegate_to_antigravity call; window
allocation between the two isn't coordinated.
delegateToAntigravityBatch([
{ task: "..." },
{ task: "...", model: "Claude Sonnet 4.6 (Thinking)" },
]);list_antigravity_files(limit?)
Every file Antigravity has generated, across every conversation, most recent first — not scoped to a single delegate call. Useful for finding something generated earlier in this session or a past one.
get_antigravity_quota()
Reads Antigravity's Settings > Models panel and returns remaining quota —
Gemini models and Claude+GPT models draw from separate pools, each with a
weekly limit and a 5-hour limit. Useful to check before delegating, or to
explain a send button is disabled error (usually means that pool is
exhausted, not that the model is unavailable).
Picking a model
Antigravity doesn't show per-message token cost, only pooled quota %. The
model parameter's description carries a rough cost/capability cheat sheet
(MODEL_GUIDE in src/antigravity-client.ts) built from third-party API
list prices and public benchmarks, researched mid-2026 — newer than this
tool's own training data, so treat it as a starting point, not gospel:
Model | ~Price (in/out per 1M tokens) | Known for |
Gemini 3.5 Flash | $1.50 / $9 | Fastest, cheapest frontier-tier option; strong coding/agentic benchmarks |
Gemini 3.1 Pro | $2 / $12 | "Thinking" mode for harder reasoning (ARC-AGI-2 ~77%) |
Claude Sonnet 4.6 | $3 / $15 | Coding quality, computer-use, agent workflows (SWE-bench ~80%) |
Claude Opus 4.6 | $5 / $25 | Strongest complex/ambiguous reasoning; legal/financial/research-grade analysis |
GPT-OSS 120B | ~$0.03–0.09 / $0.10–0.40 | Open-weight, near-o4-mini reasoning, far cheaper than the rest |
These are what the underlying models cost via their own APIs elsewhere — Antigravity itself doesn't bill you per token, it draws down the pooled quota above instead.
Known limitations
A model's quota can run out. If a non-default model (e.g. Claude Opus) has hit its usage limit, Antigravity's send button stays disabled after selecting it, even though it's still pickable in the dropdown. The tool fails fast with a clear error in that case instead of hanging until timeout — just retry later or pick a different model.
UI automation, not an API. If Google changes Antigravity's screen layout, the CSS selectors this relies on (
data-testid="send-button", the message input'saria-label, etc.) may need updating. That's the tradeoff for staying entirely inside Antigravity's terms of use — see below.Reply extraction is a text heuristic, not a structured read. It looks for the "Thought for Ns" marker Antigravity renders before its answer and takes what follows. Works reliably in testing but is the most likely thing to break on a UI redesign.
Single-window assumption. Only tested with one Antigravity window open.
What this deliberately does NOT do
No unpacking of Antigravity's app.asar, no reading of stored auth tokens,
no calling of internal/undocumented endpoints. Everything here operates
through the same interface a human uses, which is why it doesn't carry the
legal risk that reverse-engineering approaches do.
License
MIT
Available Tools
2 toolsdelegate_to_antigravityDelegate to AntigravityA
Send a task to the Google Antigravity desktop app and return its reply. Drives the real, already-logged-in app UI (types into its chat box and reads the response) — does not touch Antigravity's internals or credentials. Requires Antigravity.app to be installed and signed in on this machine.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | The task, question, or instruction to send to Antigravity. | |
| model | No | Which model Antigravity should use. If omitted, uses whatever model is currently selected in the app. Gemini 3.5 Flash ($1.50/$9 per 1M in/out) — fastest and cheapest of the group; strong coding/agentic benchmarks. Good default for quick, well-scoped tasks. Gemini 3.1 Pro ($2/$12) — adds a "thinking" mode for harder reasoning (ARC-AGI-2 ~77%); costlier for long (>200K token) context. Claude Sonnet 4.6 ($3/$15) — best-regarded for coding quality, computer-use, and multi-step agent workflows (SWE-bench ~80%). Claude Opus 4.6 ($5/$25, higher in fast mode) — strongest at complex, ambiguous, multi-step reasoning; the pick for legal/financial/research-grade analysis. GPT-OSS 120B (~$0.03-0.09/$0.10-0.40 — far cheaper than the rest) — open-weight, near-o4-mini reasoning, solid coding/tool-use. Good for high-volume or low-stakes tasks. Gemini models and Claude+GPT models draw from SEPARATE Antigravity quota pools — call getAntigravityQuota() first if unsure which pool has room. | |
| timeoutMs | No | Max time to wait for a reply, in milliseconds. Default 120000 (2 minutes). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behaviors. It clarifies that it drives the UI (not internals) and requires installation/login. However, it does not mention error behaviors, timeouts, or whether the operation is safe/idempotent. Adequate but leaves gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: two sentences plus a short requirement statement. No filler, every sentence adds essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no output schema, and one sibling, the description is largely complete. It covers the tool's function, prerequisites, and how it differs from the sibling. However, it does not specify the format of the reply (e.g., plain text, JSON), which would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds marginal value beyond the schema. The main description does not elaborate on parameters beyond what is in the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it sends a task to the Antigravity app and returns the reply. It specifies it drives the UI (types into chat box, reads response), distinguishing it from the sibling tool get_antigravity_quota, which only checks quota.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (to send a task) and mentions prerequisites (app installed and signed in). It does not explicitly list when not to use it, but the sibling tool context and the model parameter's reference to getAntigravityQuota() provide adequate guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_antigravity_quotaGet Antigravity quotaA
Read Antigravity's remaining model quota from its Settings > Models panel. Gemini models and Claude+GPT models draw from separate pools, each with a weekly limit and a 5-hour limit. Check this before delegating to Claude/GPT-OSS models if a prior call failed with a 'send button is disabled' error, or to decide which model family has room before delegating.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it is a read operation, details two separate pools with weekly and 5-hour limits. No annotations provided, so description carries full burden; it is transparent about behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, each adds value: action and source, pool details, usage guidance. No redundant text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a zero-parameter tool without output schema. Explains what, where, pools, limits, and when to use. Minor lack of return value details, but adequately informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (schema coverage 100%), so no additional parameter info needed. Baseline 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it reads remaining model quota and specifies the two separate pools (Gemini and Claude+GPT). The resource and action are unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use: before delegating after a 'send button is disabled' error, or to decide model family with room. Distinguishes from sibling tool 'delegate_to_antigravity'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.1.2- First observed
delegate_to_antigravity - First observed
get_antigravity_quota
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: one sends a task to the Antigravity app, the other reads its quota. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern with underscores: delegate_to_antigravity and get_antigravity_quota. The naming is predictable and clear.
With only 2 tools, the server feels thin. While it covers the essential operations for the Antigravity app, the scope is limited and could benefit from additional tools for a more complete interface.
The server provides the core operations (delegate a task and check quota), but it lacks tools for status checks, error recovery, or model selection. Some notable gaps exist.
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