Mengintegrasikan Gemini Cloud Assist dengan alat pihak ketiga menggunakan MCP

Dokumen ini menunjukkan cara mengintegrasikan Gemini Cloud Assist dan Application Design Center dengan alat pihak ketiga—termasuk Antigravity, Gemini CLI, dan Cursor—menggunakan Model Context Protocol (MCP).

MCP memungkinkan Anda mengintegrasikan kemampuan agentic Gemini Cloud Assist dan App Design Center langsung ke alat pihak ketiga pilihan dan alur kerja kustom sehingga Anda dapat mengakses keahlian operasi cloud tepat di tempat Anda bekerja. Untuk mengetahui detail selengkapnya tentang MCP, lihat Mengonfigurasi MCP untuk aplikasi AI Anda.

Alat MCP Gemini Cloud Assist

Gemini Cloud Assist adalah agen yang dapat diakses melalui serangkaian alat MCP menggunakan pola agen-sebagai-alat. Tidak seperti alat yang mengambil data atau melakukan pemrosesan prosedural, alat Gemini Cloud Assist mengekspos fungsi berbasis agen dari semua Gemini Cloud Assist atau alur kerja tertentu. Agen yang dipanggil oleh panggilan alat MCP membuat panggilan alatnya sendiri secara internal ke Google Cloud. Alat MCP berikut dipublikasikan agar dapat digunakan oleh agen:

ask_cloud_assist: Ini adalah antarmuka utama untuk Google Cloud bantuan dan untuk agen Gemini Cloud Assist. Semua fungsi Gemini Cloud Assist dapat diakses melalui alat ini dan mencakup fungsi alat MCP lainnya.
design_infra: Hal ini mendukung alur kerja untuk mendesain dan membangun arsitektur infrastruktur di Google Cloud. Alat ini berfungsi dengan alat server MCP Pusat Desain Aplikasi untuk mengelola siklus proses desain infrastruktur Anda—misalnya, menjalankan dan memperbaiki temuan keamanan, atau men-deploy dan memperbaiki masalah.
investigate_issue: Ini mendukung alur kerja untuk memecahkan masalah di Google Cloud. Hal ini dapat melakukan pemecahan masalah cepat atau pemecahan masalah yang lebih mendalam melalui resource Investigasi.
invoke_operation: Ini mendukung alur kerja untuk membuat, memperbarui, dan menghapus resource di Google Cloud. Alat ini hanya berfungsi jika Tindakan Agen diaktifkan. Operasi tulis di Gemini Cloud Assist hanya dapat dieksekusi melalui pemanggilan alat ini.
optimize_costs: Hal ini mendukung alur kerja untuk menganalisis, melacak, dan mengoptimalkan Google Cloud biaya. Laporan ini memberikan perincian mendetail tentang pembelanjaan dan mengidentifikasi peluang efisiensi biaya dengan menemukan resource yang tidak ada aktivitas atau kurang dimanfaatkan.

Alat MCP Application Design Center

Alat ini, yang sering diorkestrasi oleh alat design_infra Gemini Cloud Assist, mengelola siklus proses aplikasi infrastruktur menggunakan App Design Center, termasuk pengelolaan template, kepatuhan dan perbaikan keamanan, serta deployment. Alat-alat tersebut adalah:

setup_adc: Menginisialisasi lingkungan Pusat Desain Aplikasi. Ini adalah langkah penyiapan satu kali yang harus dilakukan sebelum alat Pusat Desain Aplikasi lainnya dapat digunakan.
manage_application: Mengelola siklus proses deployment aplikasi. Gunakan alat ini untuk men-deploy desain terverifikasi menggunakan Pusat Desain Aplikasi atau mengambil status dan detail deployment yang ada. Ini adalah langkah terakhir yang mengubah desain Anda menjadi infrastruktur yang di-deploy di Google Cloud.
manage_application_template: Mengelola konten Infrastructure as Code (IaC) desain infrastruktur Anda. Gunakan opsi ini untuk menyimpan desain sebagai template Pusat Desain Aplikasi, mengekspor desain sebagai file Terraform, atau memperbarui desain. Misalnya, Anda dapat menggunakan alat ini untuk memperbarui desain dengan mengubah setelan komponen dan konfigurasi parameter.
assess_best_practices: Melakukan audit keamanan dan konfigurasi yang komprehensif pada desain aplikasi Anda sebelum deployment. Alat ini memvalidasi desain berdasarkan framework Security Command Center dan kontrol yang relevan, serta menampilkan laporan dengan temuan yang dapat ditindaklanjuti untuk perbaikan.
list_application_templates: Mencantumkan semua desain aplikasi yang tersedia.

Alat MCP ini tidak boleh diperlakukan sebagai API yang stabil atau dikonfigurasi melalui klien API prosedural. Alat dari daftar ini atau parameternya dapat diganti namanya, didefinisikan ulang, dihapus, atau ditambahkan seiring waktu untuk memperhitungkan kemampuan Gemini Cloud Assist yang terus berkembang. Kemampuan Gemini Cloud Assist akan tetap stabil sehingga agen klien dapat menjalankan tugas yang sama secara konsisten, tetapi cara agen berinteraksi dengan dan memanggil Gemini Cloud Assist melalui alat ini akan berubah seiring waktu.

Sebelum memulai

Memberikan akses: Untuk menggunakan MCP, Anda harus diberi peran Identity and Access Management yang berisi izin mcp.googleapis.com/tools.call, seperti salah satu peran Gemini Cloud Assist berikut:
- roles/geminicloudassist.user
- roles/geminicloudassist.editor
- roles/geminicloudassist.admin
Jika Anda belum melakukannya, instal gcloud CLI dan komponen betanya.
Aktifkan layanan MCP di project Anda dengan perintah berikut:
```
gcloud beta services mcp enable geminicloudassist.googleapis.com \
    --project=PROJECT_ID
```
Ganti PROJECT_ID dengan project ID Google Cloud Anda.
Lakukan autentikasi menggunakan Kredensial Default Aplikasi dengan menjalankan perintah berikut:
```
gcloud auth application-default login
```

Mengonfigurasi MCP untuk klien Anda

Konfigurasi agen klien bergantung pada agen yang Anda gunakan. Bagian berikut memberikan petunjuk untuk mengonfigurasi MCP untuk berbagai klien.

Untuk mengaktifkan alur kerja desain-ke-deployment lengkap, Anda harus mengonfigurasi klien untuk terhubung ke server MCP Gemini Cloud Assist dan App Design Center, serta menyertakan file SKILL.md yang relevan. Cuplikan berikut menunjukkan konfigurasi gabungan.

Gemini CLI

Mengonfigurasi menggunakan ekstensi

Instal server MCP sebagai ekstensi Gemini CLI menggunakan perintah berikut:

gemini extensions install https://github.com/GoogleCloudPlatform/gemini-cloud-assist-mcp

Melakukan konfigurasi secara manual

Konfigurasi setelan server MCP.

Buka file setelan cakupan pengguna Gemini CLI di ~/.gemini/settings.json atau setelan cakupan ruang kerja Anda di .gemini/settings.json. Jika file tidak ada, buat file. Tambahkan atau gabungkan blok konfigurasi berikut di bawah kunci mcpServers. Pastikan file menggunakan format JSON yang valid. Konfigurasi ini otomatis menggunakan Kredensial Default Aplikasi Anda.

{
  "mcpServers": {
    "gemini_cloud_assist": {
      "httpUrl": "https://geminicloudassist.googleapis.com/mcp",
      "authProviderType": "google_credentials",
      "oauth": {
        "scopes": ["https://www.googleapis.com/auth/cloud-platform"]
      },
      "timeout": 600000
    },
    "application_design_center": {
      "httpUrl": "https://designcenter.googleapis.com/mcp",
      "authProviderType": "google_credentials",
      "oauth": {
        "scopes": ["https://www.googleapis.com/auth/cloud-platform"]
      },
      "timeout": 600000
    }
  }
}

Buat dan isi file skill Anda.

Buat file SKILL.md di salah satu direktori berikut. Salin dan tempel konten markdown untuk keterampilan yang Anda pilih dari bagian referensi keterampilan dalam dokumen ini ke dalam file.
- Cakupan ruang kerja: .gemini/skills/<skill-name>/
- Cakupan pengguna: ~/.gemini/skills/<skill-name>/
Berikut adalah contoh struktur direktori:
```
.gemini/
└── skills/
    ├── design-and-deploy/
    │   └── SKILL.md
    └── google-gemini-cloud-assist/
        └── SKILL.md
              
```
Verifikasi keterampilan Anda.

Setelah Anda memodifikasi file settings.json dan SKILL.md, mulai ulang Gemini CLI. Gunakan perintah berikut untuk memverifikasi:
- /skills list: Untuk memverifikasi bahwa keterampilan lokal Anda telah berhasil ditemukan.
- /mcp list: Untuk memverifikasi bahwa server MCP Gemini Cloud Assist dan Application Design Center terhubung.

Antigravity

Instal ekstensi.

Di Antigravity, buka marketplace Ekstensi. Telusuri ekstensi Gemini Cloud Assist, klik Instal, lalu klik Muat ulang.

Konfigurasi server MCP secara manual.

Buka panel Manage MCP Servers dan klik View raw config untuk mengedit file mcp_config.json. Tambahkan atau gabungkan blok konfigurasi berikut. Penyiapan ini menggunakan Kredensial Default Aplikasi Anda untuk autentikasi.

{
  "mcpServers": {
    "gemini_cloud_assist": {
      "serverUrl": "https://geminicloudassist.googleapis.com/mcp",
      "headers": {},
      "authProviderType": "google_credentials"
    },
    "application_design_center": {
      "serverUrl": "https://designcenter.googleapis.com/mcp",
      "headers": {},
      "authProviderType": "google_credentials"
    }
  }
}

Verifikasi penyiapan Anda.

Setelah Anda mengubah file mcp_config.json dan SKILL.md, mulai ulang Antigravity. Di panel Agen, gunakan perintah garis miring berikut untuk memverifikasi koneksi:
- /skills list: Memverifikasi bahwa kemampuan lokal Anda telah ditemukan.
- /mcp list: Memverifikasi bahwa server MCP Gemini Cloud Assist dan Application Design Center terhubung.

Kursor

Di project Google Cloud Anda, buat client ID OAuth 2.0 untuk aplikasi desktop.
Konfigurasi URI://anysphere.cursor-mcp/oauth/callback sebagai URL pengalihan. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi kursor.

Tambahkan atau gabungkan blok konfigurasi berikut:

   {
     "mcpServers": {
       "gemini_cloud_assist": {
         "url": "https://geminicloudassist.googleapis.com/mcp",
         "auth": {
           "CLIENT_ID": "${env:OAUTH_CLIENT_ID}",
           "CLIENT_SECRET": "${env:OAUTH_CLIENT_SECRET}",
           "scopes": ["https://www.googleapis.com/auth/cloud-platform"]
         }
       }
     }
   }

Kemampuan Gemini Cloud Assist

Alat MCP Gemini Cloud Assist dan App Design Center menggunakan keterampilan agen untuk menginstruksikan agen Anda. Keahlian memandu agen Anda untuk melakukan tugas berikut:

Rangkai beberapa alat menjadi satu aliran kerja.
Memproses informasi lokal yang relevan, seperti kode deployment Terraform, untuk mempertimbangkan konteks lokal Anda.
Menanggapi pemanggilan eksplisit saat Anda merujuk langsung ke skill.

Saat menggunakan agen interaktif seperti Antigravity atau Gemini CLI, Anda dapat mengintegrasikan alat MCP Gemini Cloud Assist dengan agen dan alur kerja lainnya. Karena alat ini menyediakan kemampuan luas yang mungkin tumpang-tindih dengan alat khusus yang ada, gunakan strategi berikut untuk mengoptimalkan lingkungan dan kasus penggunaan spesifik Anda:

Menyesuaikan definisi skill: Perbarui file SKILL.md untuk menentukan maksud dan kasus penggunaan yang ingin Anda gunakan untuk memanggil Gemini Cloud Assist. Hal ini memastikan bahwa agen hanya dipicu untuk tugas yang memerlukan keahlian cloud yang luas.
Memanggil skill secara eksplisit: Banyak agen interaktif memungkinkan Anda mengontrol kapan skill dipicu. Jika agen Anda mendukungnya, Anda dapat mengonfigurasi keterampilan untuk memerlukan pemanggilan manual menggunakan perintah garis miring (/) atau sebutan @ (@). Dengan pendekatan ini, Anda dapat memilih secara tepat kapan menggunakan Gemini Cloud Assist dan kapan menggunakan alat khusus lainnya.

Referensi kemampuan Gemini Cloud Assist

Kami merekomendasikan file SKILL.md berikut di agen mana pun yang dikonfigurasi dengan alat MCP Gemini Cloud Assist. File ini memandu agen Anda tentang cara menggunakan alat dengan tepat.

Mendesain dan men-deploy infrastruktur

---
name: designing-and-deploying-infrastructure
description: This skill is used to design, assess, deploy, and troubleshoot cloud infrastructure using the Application Design Center (ADC).
---
# Designing and Deploying Infrastructure
## Index
1. [Overview](#overview)
2. [Best Practices & Constraints](#best-practices--constraints)
3. [Phase 1: Infrastructure Design & Refinement](#phase-1-infrastructure-design--refinement)
4. [Phase 2: Best Practices Assessment & Design Iteration](#phase-2-best-practices-assessment--design-iteration)
5. [Phase 3: Application Deployment](#phase-3-application-deployment)
6. [Phase 4: Get Deployed Resources](#phase-4-get-deployed-resources)
7. [Phase 5: Troubleshoot Deployment Failures](#phase-5-troubleshoot-deployment-failures)
8. [Phase 6: Verification & E2E Testing](#phase-6-verification--e2e-testing)

## Overview
This skill provides a prescriptive, multi-loop workflow for the entire infrastructure lifecycle on Google Cloud Platform (GCP). It leverages the Gemini Application Designer (GAD) and Application Design Center (ADC) tools (like `gemini_cloud_assist:design_infra` and `application_design_center:assess_best_practices`) to intelligently design architectures, assess best practices, and automate deployment and troubleshooting.
Always maintain the persona of a Principal Cloud Architect. Delegate all research and design to the specialized tools provided.
**Note:** These tools are part of the `gemini_cloud_assist` and `application_design_center` MCP Servers. Tool names are qualified with their respective server names (e.g., `gemini_cloud_assist:tool_name`).


## Best Practices & Constraints
-   **Delegation & No Manual Design**: Delegate all architecture decisions and product selections to the `gemini_cloud_assist:design_infra` tool. **Do not** design manually or edit generated Terraform code. Request changes via the tool.
-   **Inputs**: Always ask the user for required context like project ID, service accounts, etc. if necessary -- do not make assumptions or use placeholders.
-   **Visualization Power**: Always render the Mermaid diagram from `gemini_cloud_assist:design_infra` in every implementation plan. Refresh the diagram after every design change. Do not create your own diagrams.
-   **Loop Discipline**: Follow the workflow loops and exit criteria strictly. If you cannot follow the Infrastructure Lifecycle Workflow, you must exit and inform the user. For example:
-   **Application Template as the main resource**: The application template is the main resource when generating and iterating on a design. **Always** look for the application template URI in the `gemini_cloud_assist:design_infra` response (`serializedApplicationTemplateURI`), and use that for the rest of the Infrastructure Lifecycle Workflow.
    -   **Application Template vs Application**: Application template is a template that is used to create an application. Application is an instance of an application template. Do not confuse these two.
    -   **Never** attempt to create an application template URI yourself; always use the URI returned from `gemini_cloud_assist:design_infra`.


## Infrastructure Lifecycle Workflow
### Phase 1: Infrastructure Design & Refinement
**Goal**: Transform vague user requirements into a concrete, approved architectural design.
1.  **Requirement Gathering**: Capture user intent if it's vague (e.g., "3-tier web app with high availability").
Guidelines for initial design that **you must follow**:
    - Gather project ID (required) and ADC space ID (optional) from the user.
2.  **Codebase Analysis**: Critically, **before** calling `gemini_cloud_assist:design_infra`, you **must** perform a thorough exhaustive analysis of the user's application codebase (if available). **Do not** stop at high-level documentation or configuration files at the surface level; you **must** inspect the full depth of the codebase, including business logic, to find hidden API clients, dependencies, environment variables, and other application code context required to design cloud infrastructure. The list below outlines critical checks you **must** perform. **Do not** limit your investigation to only these items:
    - If you scan the application code to provide additional context, summarize *only* the application's characteristics (e.g., languages, frameworks, statefulness) and **do not** assume or suggest specific infrastructure components or services (e.g., **do not** choose the product type (e.g. GKE, Cloud Run, etc) or network information unless the user explicitly requested them or they are specified in codebase).
    - **NEVER** rely solely on `grep_search`, `os.environ`, or summary documentation (like READMEs) to determine infrastructure needs. **DO NOT** make premature assumptions or over-optimize for speed. Accuracy is critical; you **must** inspect the entire codebase.
    - Identify required environment variables, secrets, ports exposed in application, and database connection patterns. Environment Variables may also be present in nginx config files. Environment variables are critical for architecture so double check you have identified all of them.
    - Identify all the dependencies on existing GCP services (e.g., Vertex AI API, pre-existing GCS buckets). Along with the rest of the codebase, you **must** read dependency files (e.g., `requirements.txt`, `package.json`, `go.mod`) to identify GCP SDKs used and scan source code for explicit GCP client initializations.
    - Scan the codebase (e.g., `cloudbuild.yaml`, `Dockerfile`, CI/CD configs) to extract container image URLs. If no image is found or if the identified image does not exist in the Artifact Registry, you must build and upload the image. Provide only the resulting URL to `gemini_cloud_assist:design_infra`.
3.  **Initial Design**: Call `gemini_cloud_assist:design_infra` tool with `command="manage_app_design"` and the user's query. Ensure that `project` is `projects/{project_id}`
    - **MANDATORY - PRODUCT AGNOSTICISM**: Do NOT architect the infrastructure prematurely (e.g. "frontend should be a Cloud Run service"), instead delegate this responsibilty entirely to `gemini_cloud_assist:design_infra` tool. The codebase analysis results __may suggest__ certain product types (e.g. database uses the PostgreSQL client library, therefore database may be PostgreSQL), but otherwise make **zero** assumptions about the architecture. Since environment variables are critical to the architecture, you **must** pass the full set discovered during codebase analysis phase in all the design_infra manage_app_design requests.
    - **Prevent Naming Collisions**: Generate a unique, non-sequential five-character alphanumeric suffix, strictly avoiding common patterns like `12345`, `abcde` or `a1b2c`. Instruct `gemini_cloud_assist:design_infra` in the prompt to append this suffix to all newly created components.
4.  **Visualization, Terraform Code and Verification**: The `gemini_cloud_assist:design_infra` will respond with a Mermaid diagram, Terraform code, and the `serializedApplicationTemplateURI`.
    - Retrieve the Terraform code corresponding to each `.tf` file referenced and save them into a dedicated directory called `infra`.
    - You **must** present an Implementation Plan that contains the Mermaid diagram rendered within the Implementation Plan, as well as the location of the Terraform configs.
    - **Verification Step**: This is a critical step. You **must** meticulously verify the generated Terraform code to ensure it includes all the environment variables, secrets, dependencies, and correct container image URLs required by application, identified during the Codebase Analysis step. If *any* required configurations are missing or incorrect, you **must** iterate on the design using `gemini_cloud_assist:design_infra command="manage_app_design"` to correct them. **Do not proceed to deployment with an unverified or incomplete design.**
5.  **Iteration**: Refine the design by feeding user feedback back into `gemini_cloud_assist:design_infra`.
    -   **Design Iteration Constraints**: When iterating on the design, **do not** make manual edits to the Terraform code. **Always** use `gemini_cloud_assist:design_infra` tool exclusively to update the Terraform code.  **Always** keep the local Terraform code in sync with Terraform code returned from `gemini_cloud_assist:design_infra`.
    -   **Visualization Update**: Update the Implementation Plan by re-rendering the Mermaid diagram and updating the Terraform configs.
    -   **Retries**: After encountering transient errors and you need to retry `gemini_cloud_assist:design_infra` calls, you **must make exactly the same call** with **same** arguments.
6.  **Exit Criteria**:
    -   Design fulfills all critical requirements and has resources needed by
        the application, especially the environment variables for each service.
    -   User confirms the design is satisfactory.
    -   `gemini_cloud_assist:design_infra` reaches a stable state with no further architectural changes.
### Phase 2: Best Practices Assessment & Design Iteration
**Goal**: Validate design alignment with security, cost, and reliability benchmarks prior to deployment.
1. **Execution**: Invoke `assess_best_practices` using the application template metadata (project, location, space and application template identifiers).
2. **Analysis**: You **MUST** present all findings to the user in a pretty tabular fashion per framework, detailing specific violations, and their associated severity levels - before proceeding to remediation.
3. **Remediation Loop**:
    - Pass identified findings as context to `gemini_cloud_assist:design_infra(command="manage_app_design")`.
    - **Strict Constraint**: All modifications must be executed via the `gemini_cloud_assist:design_infra` tool; manual Terraform manipulation is prohibited.
    - Perform a diff-based comparison between current and updated Terraform code for user approval.
    - Re-assess post-update to verify the resolution of findings.
4. **Exit Criteria**:
    - **Optimization**: Zero findings remaining.
    - **Convergence**: `gemini_cloud_assist:design_infra` provides no further suggestions.
    - **Threshold**: Maximum of three (3) iterative attempts reached.
5. **Transition**: Proceed to Phase 3 deployment only upon loop termination.
### Phase 3: Application Deployment
**Goal**: Deploy the application template to the GCP environment.
1.  **Deploy Application**: Use the `application_design_center:manage_application` tool with the `APPLICATION_OPERATION_DEPLOY` operation to deploy the application.
    *   **Required Arguments**: `project`, `location`, `spaceId`, `applicationTemplateUri`, `applicationId`, `serviceAccount`.
    *   **Note**: This returns a Long-Running Operation (LRO). Inform the user that the deployment has started.
    *   **Example**:
        ```json
        {
          "project": "my-project",
          "location": "us-central1",
          "spaceId": "my-space",
          "applicationId": "my-app",
          "operation": "APPLICATION_OPERATION_DEPLOY",
          "applicationTemplateUri": "projects/my-project/locations/us-central1/spaces/my-space/applicationTemplates/my-template",
          "serviceAccount": "projects/my-project/serviceAccounts/deployer@my-project.iam.gserviceaccount.com"
        }
        ```
2.  **Monitor Deployment**: Repeatedly poll the LRO (e.g., every 30-60 seconds) until `done: true`.
3.  **Handle Results**:
    -   **Success**: If `done` is `true` and there is no `error` field, proceed to Phase 4.
    -   **Failure**: If an `error` field is present, proceed to Phase 5.
### Phase 4: Get Deployed Resources
1.  **Retrieve Information**: Call the `application_design_center:manage_application` tool with the `APPLICATION_OPERATION_GET` operation, providing `project`, `location`, `spaceId` and `applicationId`, to fetch the outputs and status of the deployed resources.
    *   **Example**:
        ```json
        {
          "project": "my-project",
          "location": "us-central1",
          "spaceId": "my-space",
          "applicationId": "my-app",
          "operation": "APPLICATION_OPERATION_GET"
        }
        ```
2.  **User Confirmation**: Present the deployed resource information to the user and conclude the task.
### Phase 5: Troubleshoot Deployment Failures
**Goal**: Diagnose and remediate deployment failures iteratively.
When troubleshooting a failed application, follow these steps.
**Process:**
1.  **Initiate Troubleshooting:**
    - **Action:** Call `gemini_cloud_assist:design_infra` with `applicationUri` (format: projects/{project}/locations/{location}/spaces/{spaceId}/applications/{applicationId}) to get suggested fixes.
    - Convert the returned response to JSON. The value of a parameter may be a JSON string that needs to be parsed.
    - **Expected Output Structure from Troubleshooting:**
      ```json
      {
        "summary": "...",
        "troubleshootingSteps": [
          {
            "description": "...",
            "gcloud_commands": ["gcloud ..."],
            "componentParameters": null
          },
          {
            "description": "...",
            "gcloud_commands": null,
            "componentParameters": [
              {
                "componentUri": "projects/.../components/comp-a",
                "parameters": [
                  { "key": "param1", "value": "new_value" },
                  { "key": "param3", "value": "another" }
                ]
              }
            ]
          }
        ]
      }
      ```
2.  **Analyze Troubleshooting Steps**: Iterate through each `step` in `troubleshootingSteps`. Identify if `gcloud_commands` or `component_parameters` are provided.
3.  **Update Application Template**:
    - **If `component_parameters` updates are suggested**:
      1. Get the `application_template_id` from the Application object obtained in Step 2. The value is present at `serialized_application_template.uri`. Get the `application_template_id` from the uri.
      2. **Update Application Template**: Call `application_design_center:manage_application_template` with `APPLICATION_TEMPLATE_OPERATION_UPDATE_COMPONENT_PARAMETERS`.
         - **Tool**: `application_design_center:manage_application_template`
         - **Arguments**: `parent`, `applicationTemplateId`, `componentParameters`, `operation` - `APPLICATION_TEMPLATE_OPERATION_UPDATE_COMPONENT_PARAMETERS`
      3. **Example**:
         ```json
         {
           "applicationTemplateId": "my-template",
           "componentParameters": [
             {
               "component": "projects/my-project/locations/us-central1/spaces/my-space/applicationTemplates/my-template/components/cloud-run-1",
               "parameters": [
                 {
                   "key": "containers",
                   "value": [
                     {
                       "container_image": "us-docker.pkg.dev/cloudrun/container/hello",
                       "container_name": "service-container",
                       "ports": {
                         "container_port": 8080,
                         "name": "http1"
                       },
                       "resources": {
                         "cpu_idle": false,
                         "limits": {
                           "cpu": "4",
                           "memory": "16Gi"
                         },
                         "startup_cpu_boost": false
                       }
                     }
                   ]
                 }
               ]
             }
           ],
           "operation": "APPLICATION_TEMPLATE_OPERATION_UPDATE_COMPONENT_PARAMETERS",
           "parent": "projects/my-project/locations/us-central1/spaces/my-space"
         }
         ```
4.  **Retry Deployment**:
    - **Action**: Attempt to deploy the application again.
    - **Tool**: `application_design_center:manage_application`
    - **Arguments**: `project`, `location`, `spaceId`, `applicationTemplateUri`, `applicationId`, `serviceAccount`. `operation` - `APPLICATION_OPERATION_DEPLOY`
5.  **Iterate if Necessary**: If deployment fails again, repeat troubleshooting steps up to 5 times. Report the history to the user if the limit is reached.
**Operational Notes:**
* Mutating tool calls often return an LRO.
* Always poll LROs on behalf of the user; do not ask the user to run the polling command.
* Do not sleep during deployment status polling. Poll actively every 30-60 seconds until the LRO is done.
* Apply `debug_deployment` responses exactly as provided.
* For retries, disregard previous attempts and start from Step 2 again.
* Handle bad-gateway errors during commit with jittered retries and verify the template revision.
* Exclude `PORT` from being explicitly set as a Cloud Run environment variables as it is a reserved name.
### Phase 6: Verification & E2E Testing
Following a successful deployment:
    - Verify that services are using correct container image URLs and that each component is healthy.
    - Conduct a simple demo test to ensure E2E functionality and validate the hosted services are working as expected.

Beroperasi di Google Cloud

---
name: operating-google-cloud
description: Comprehensive management of GCP resources and Kubernetes. Supports direct tool delegation for assistance, infrastructure design, troubleshooting, cost optimization, and resource mutations.
---

# Managing GCP and Kubernetes

## Index
1. [Overview](#overview)
2. [Tool Usage & Routing](#tool-usage--routing)
3. [Instructions and Constraints](#instructions-and-constraints)


GCP and Kubernetes resources and mutations should use MCP tools. These tools are specialized backend agents. As the client agent, your role is to identify the intent and delegate the entire task verbatim to the appropriate intelligent tool.

**Note:** These tools are part of the `gemini-cloud-assist` and `application-design-center` MCP Servers. Tool names are qualified with their respective server names (e.g., `gemini_cloud_assist:tool_name`).



## Tool Usage & Routing

1.  **General Assistance & Orchestration**: Use `gemini_cloud_assist:ask_cloud_assist` for general inquiries, estate queries (e.g., "List my active VMs"), low-complexity directives, and triage of ambiguous issues.
    *   *Triggers*: Broad questions, status checks, exploratory queries, mutative directives (e.g., "Restart VM", "Scale cluster").
    *   *Routing*: This is the **default choice** for intents that are purely informational or involve general cloud concepts.

2.  **Infrastructure Design & Architecture**: Use `gemini_cloud_assist:design_infra` for designing, architecting, or deploying infrastructure.
    *   *Commands*: 
        - `manage_app_design`: Use for comprehensive ADC application designs and IaC imports.
        - `generate_terraform`: Use for single-resource Terraform generation.
        - `generate_gcloud` / `generate_bigquery`: Use for generating CLI commands.
        - `generate_kubernetes_yaml`: Use for Kubernetes manifests.
        - `debug_deployment`: Use for debugging ADC deployment failures.

3.  **Troubleshooting & Deep Diagnostics**: Use `gemini_cloud_assist:investigate_issue` for specialized "SRE in a box" diagnostics and Root Cause Analysis (RCA).
    *   *Triggers*: Outages, crashes, latency spikes, deep debugging, error log reasoning, and understanding application dependencies (topology).
    *   *Routing*: Use when the user asks "Why" a failure occurred or needs remediation for a known incident.

4.  **Cost Optimization**: Use `gemini_cloud_assist:optimize_costs` for analyzing spend and identifying savings.
    *   *Triggers*: Spending analysis, top cost drivers, identifying idle or underutilized resources for the purpose of saving money.
    *   *Constraints*: This tool is **read-only**; it provides analysis but does not execute changes. Do not use for general utilization queries unrelated to cost.

5.  **GKE Resource Mutations**: Use `gemini_cloud_assist:invoke_operation` specifically for GKE operations.
    *   *Operation Types*: `GKE_APPLY` (for raw YAML manifests) and `GKE_PATCH` (for specific JSON patches).
    *   *Input Requirement*: The `userQuery` must be a stringified JSON object containing `operation_type` and the corresponding `gke_apply` or `gke_patch` details.



## Instructions and Constraints
When answering any queries about Google Cloud Platform (GCP), YOU MUST follow these rules:

- **Context ID Persistence:** All Gemini Cloud Assist (GCA) tools return a `contextId` in their response. Ensure that any subsequent turns to a GCA tool pass this `contextId` as a parameter to maintain multi-turn conversation state.
- **Direct Tool Mapping:** Upon receiving a request about GCP resources, immediately invoke the relevant tool identified in the Routing section.
- **Context Integrity:** Pass the user's entire prompt exactly as provided; maintain the original query without summarization, rephrasing, or truncation.
- **Atomic Delegation:** Execute the request as a single turn; delegate the entire problem to the tool rather than breaking it down into intermediate queries.
- **Identifier Precision:** Include all identifiers (Project IDs, Cluster names, or Instance paths) discovered in the prompt.
- **Local Context Enrichment:** Forward the user's query to the tools along with any additional relevant local context identified in the working directory.
- **Persistence to Local Workspace:** Save any configuration, manifest, or YAML generated or updated by the tools to the local workspace.
- **Tool Exclusivity:** Rely exclusively on the provided MCP tools for cloud interactions; refrain from executing local commands such as `gcloud` or `kubectl`.
- **Remote Source of Truth:** Rely solely on MCP tools to discover and validate the existence of cloud resources; bypass local validation against configuration files like `.gcloud/config` or `kubeconfig`.
- **Execution Workflow:** Always ask the user if they want to apply the YAML using `gemini_cloud_assist:invoke_operation` after generation. Explicitly ask: "Would you like to proceed with applying this configuration?". If the user confirms, invoke the tool to apply the manifests.

Langkah berikutnya

Melihat setelan administrator Gemini untuk Google Cloud .