Nano Banana MCP Server 支持 Claude、Cursor 和 Gemini CLI

Settings → Connectors → Add custom connector
URL: https://bananabanana.pro/api/mcp
→ Add → Connect → approve access on bananabanana.pro

# with an API key
claude mcp add --transport http bananabanana https://bananabanana.pro/api/mcp \
  --header "Authorization: Bearer bb_live_YOUR_KEY"

# or with OAuth — no key, sign in in the browser
claude mcp add --transport http bananabanana https://bananabanana.pro/api/mcp
# then run /mcp inside Claude Code and pick "Authenticate"

{
  "mcpServers": {
    "bananabanana": {
      "command": "npx",
      "args": [
        "-y", "mcp-remote", "https://bananabanana.pro/api/mcp",
        "--header", "Authorization: Bearer bb_live_YOUR_KEY"
      ]
    }
  }
}

{
  "mcpServers": {
    "bananabanana": {
      "url": "https://bananabanana.pro/api/mcp",
      "headers": {
        "Authorization": "Bearer bb_live_YOUR_KEY"
      }
    }
  }
}

{
  "servers": {
    "bananabanana": {
      "type": "http",
      "url": "https://bananabanana.pro/api/mcp",
      "headers": {
        "Authorization": "Bearer ${input:bb-api-key}"
      }
    }
  },
  "inputs": [
    {
      "type": "promptString",
      "id": "bb-api-key",
      "description": "BananaBanana API key (bb_live_…)",
      "password": true
    }
  ]
}

{
  "mcpServers": {
    "bananabanana": {
      "serverUrl": "https://bananabanana.pro/api/mcp",
      "headers": {
        "Authorization": "Bearer bb_live_YOUR_KEY"
      }
    }
  }
}

{
  "mcpServers": {
    "bananabanana": {
      "type": "streamableHttp",
      "url": "https://bananabanana.pro/api/mcp",
      "headers": {
        "Authorization": "Bearer bb_live_YOUR_KEY"
      },
      "disabled": false,
      "autoApprove": ["list_models", "get_account", "get_result"]
    }
  }
}

POST https://bananabanana.pro/api/mcp
Authorization: Bearer bb_live_YOUR_KEY
Content-Type: application/json
Accept: application/json, text/event-stream

{"jsonrpc":"2.0","id":1,"method":"tools/list"}

[mcp_servers.bananabanana]
url = "https://bananabanana.pro/api/mcp"
bearer_token_env_var = "BB_API_KEY"

# start an image generation (charges one image, $0.03 on the default Lite model)
curl -s https://bananabanana.pro/api/mcp \
  -H "Authorization: Bearer bb_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{
        "name":"generate_image",
        "arguments":{"prompt":"studio photo of a ceramic mug on linen, soft daylight"}}}'

# → result.structuredContent.job_id = "cmxy…"

# fetch the result (long-polls server-side up to 30 s; free)
curl -s https://bananabanana.pro/api/mcp \
  -H "Authorization: Bearer bb_live_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{
        "name":"get_result",
        "arguments":{"job_id":"cmxy…","wait_seconds":30}}}'

import requests

MCP = "https://bananabanana.pro/api/mcp"
HEADERS = {"Authorization": "Bearer bb_live_YOUR_KEY"}

def call(tool, **args):
    r = requests.post(MCP, headers=HEADERS, json={
        "jsonrpc": "2.0", "id": 1, "method": "tools/call",
        "params": {"name": tool, "arguments": args},
    })
    r.raise_for_status()
    return r.json()["result"]["structuredContent"]

job = call("generate_image", prompt="watercolor painting of a lighthouse at dawn")

result = call("get_result", job_id=job["job_id"], wait_seconds=30)
while result["status"] == "processing":
    result = call("get_result", job_id=job["job_id"], wait_seconds=30)

print(result["files"][0]["url"], "cost:", result["cost_charged_usd"])

const MCP = "https://bananabanana.pro/api/mcp";

async function call(tool: string, args: Record<string, unknown>) {
  const res = await fetch(MCP, {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.BB_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      jsonrpc: "2.0", id: 1, method: "tools/call",
      params: { name: tool, arguments: args },
    }),
  });
  const { result } = await res.json();
  return result.structuredContent;
}

const job = await call("generate_image", {
  prompt: "isometric 3D render of a tiny greenhouse at golden hour",
});

let out = await call("get_result", { job_id: job.job_id, wait_seconds: 30 });
while (out.status === "processing") {
  out = await call("get_result", { job_id: job.job_id, wait_seconds: 30 });
}
console.log(out.files[0].url, "cost:", out.cost_charged_usd);

→ generate_video {"prompt": "drone shot over a misty pine forest", "model": "veo-3.1-fast"}
← {"status": "confirmation_required", "quoted_cost_usd": 0.70, ...}
→ generate_video {..., "confirm_cost": 0.70}
← {"job_id": "cmxy…", "status": "processing", "cost_charged_usd": 0.70, "balance_remaining_usd": 12.40}
→ get_result {"job_id": "cmxy…"}
← {"status": "completed", "files": [{"url": "https://…"}], "cost_charged_usd": 0.70}

→ generate_speech {"text": "Hi, I'm Mia — today we test three budget tripods.", "voice": "Kore"}
← {"job_id": "cmsp…", "status": "completed", "url": "https://…/voice.wav"}
→ generate_video {"model": "wan-3.0", "duration": 6, "resolution": "720p", "aspect_ratio": "9:16",
                  "reference_images": ["cmimg…"], "reference_audios": ["cmsp…"],
                  "prompt": "The presenter from Image 1 looks into the camera and says: "First up — the lightest one.""}
← {"status": "confirmation_required", "quoted_cost_usd": 0.60, "input_audio_seconds": 3.4,
   "voice_reference_note": "Voice samples are not billed. The character will speak with the timbre of Audio 1; …"}
→ generate_video {..., "confirm_cost": 0.60}
← {"job_id": "cmxy…", "status": "processing"}   // repeat with new lines — same voice, same face