Alibaba Cloud Ops MCP Server vs GCP Platform Engineer
A side-by-side look at the Alibaba Cloud Ops MCP Server and GCP Platform Engineer MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Alibaba Cloud Ops MCP Server for cloud platforms work as a local process; choose GCP Platform Engineer for cloud platforms as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Alibaba Cloud Ops MCP Server when
- You need dedicated capabilities in the Cloud Platforms domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
ALIBABA_CLOUD_ACCESS_KEY_ID, ALIBABA_CLOUD_ACCESS_KEY_SECRET. - You want tools like
OOS_RunCommand, OOS_StartInstances, OOS_StopInstances.
Choose GCP Platform Engineer when
- You need dedicated capabilities in the Cloud Platforms domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
PLATFORM_MCP_CONFIG, PLATFORM_MCP_ENVIRONMENTS, PLATFORM_MCP_DEFAULT_ENVIRONMENT, GOOGLE_APPLICATION_CREDENTIALS. - You want tools like
list_environments, query_logs, get_recent_errors.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Cloud Platforms | Cloud Platforms |
| Quality signal | 65/100 · Great | 59/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | API key required | Not declared |
| Pricing | BYOK (pay the provider) | Not declared |
| Required env vars | ALIBABA_CLOUD_ACCESS_KEY_IDALIBABA_CLOUD_ACCESS_KEY_SECRET | PLATFORM_MCP_CONFIGPLATFORM_MCP_ENVIRONMENTSPLATFORM_MCP_DEFAULT_ENVIRONMENTGOOGLE_APPLICATION_CREDENTIALS |
| Verified owner | Community listing | Community listing |
| GitHub stars | 132 | 2 |
| Directory activity | 10 views 0 installs 0 upvotes | 2 views 0 installs 0 upvotes |
| Tools | 40 | 16 |
| Full listing | View Alibaba Cloud Ops MCP Server | View GCP Platform Engineer |
Tools & capabilities
Alibaba Cloud Ops MCP Server tools 40
OOS_RunCommand批量在多台ECS实例上运行云助手命令，适用于需要同时管理多台ECS实例的场景，如应用程序管理和资源标记操作等。OOS_StartInstances批量启动ECS实例，适用于需要同时管理和启动多台ECS实例的场景，例如应用部署和高可用性场景。OOS_StopInstances批量停止ECS实例，适用于需要同时管理和停止多台ECS实例的场景。OOS_RebootInstances批量重启ECS实例，适用于需要同时管理和重启多台ECS实例的场景。OOS_RunInstances批量创建ECS实例，适用于需要同时创建多台ECS实例的场景，例如应用部署和高可用性场景。OOS_ResetPassword批量修改ECS实例的密码，请注意，本操作将会重启ECS实例OOS_ReplaceSystemDisk批量替换ECS实例的系统盘，更换操作系统OOS_StartRDSInstances批量启动RDS实例，适用于需要同时管理和启动多台RDS实例的场景，例如应用部署和高可用性场景。OOS_StopRDSInstances批量停止RDS实例，适用于需要同时管理和停止多台RDS实例的场景。OOS_RebootRDSInstances批量重启RDS实例，适用于需要同时管理和重启多台RDS实例的场景。OOS_CodeDeploy将应用部署到阿里云ECS实例。使用阿里云OOS（运维编排服务）的CodeDeploy功能实现自动化部署。 ## 前置条件（调用此工具前需完成） 1. **识别项目类型**：读取项目配置文件（package.json、pom.xml、requirements.txt等），确定技术栈和部署语言（通过deploy_language参数传入） 2. **构建部署产物**：执行构建命令生成压缩包（tar.gz、zip等），保存到 `.code_deploy/release` 目录 3. **准备ECS实例**：确保目标ECS实例已创建，获取实例ID列表 ## ⚠️ 重要提示：启动和停止命令参数 **正常情况下，不需要提供 application_start 和 application_stop 参数！** - 系统会自动通过规则引擎分析部署文件和 deploy_language，自动生成启动和停止命令 - 自动生成的命令包含所有必要的防御性检查（文件存在性、路径存在性等） - 自动生成的命令正确处理压缩包解压、目录切换、后台运行、日志重定向等 - 对于 Node.js 应用，自动生成的命令会先执行 `npm install` 安装依赖 **只有在以下情况下才需要手动提供这两个参数：** 1. 用户明确指定了自定义的启动/停止命令 2. 用户提供了关于启动/停止命令的特殊要求或说明 **调用建议：直接调用工具，不传 application_start 和 application_stop 参数即可。** ## 自动生成的命令规范（供参考，了解即可） ### 1. 防御性命令设计 自动生成的命令包含存在性检查，避免因路径/文件/命令不存在导致失败： - 压缩包：先检查文件存在再解压 `[ -f app.tar.gz ] && tar -xzf app.tar.gz || exit 1` - 可执行文件：检查文件存在再执行 `[ -f start.sh ] && chmod +x start.sh && ./start.sh || exit 1` - 命令可用性：检查命令是否存在 `command -v npm >/dev/null 2>&1 || exit 1` - 目录切换：先验证路径存在再切换 `[ -d dir ] && cd dir || exit 1` ### 2. 压缩包处理规范 如果产物是压缩包，自动生成的命令会先解压： - 使用非交互式命令：`tar -xzf`、`unzip -o`（自动覆盖，无需确认） - 解压后执行启动命令，确保路径对应 - 示例：`tar -xzf app.tar.gz && [ -d app ] && cd app && nohup java -jar app.jar > /root/app.log 2>&1 &` ### 3. 后台运行与日志 自动生成的启动命令使用后台运行并重定向日志： - 格式：`nohup <command> > /root/app.log 2>&1 &` - 说明：nohup保持后台运行，`>` 重定向标准输出，`2>&1` 合并错误输出，`&` 后台执行 ## 注意事项 - 应用和应用分组会自动检查，已存在则跳过创建 - 未提供ECS实例ID时，工具会返回创建引导链接 - 部署信息自动保存到 `.code_deploy/.application.json` - project_path未提供时，会从file_path推断或使用当前目录 - 部署完成后，以markdown格式展示service_link供用户跳转OOS_GetLastDeploymentInfo获取上次部署的应用信息- +28 more on the listing page
GCP Platform Engineer tools 16
list_environmentsList the GCP environments this server can query and which is the default. Pass one of the returned names as the `environment` argument of any other tool to target that project (e.g. environment='production'). Omitting the argument uses the default environment shown here. Common shorthands such as 'prod' and 'stg' are accepted, as is a bare project id.query_logsQuery Cloud Logging with an advanced-filter expression. Args: filter: Cloud Logging advanced filter (e.g. 'severity>=WARNING AND resource.type="cloud_run_revision"'). Leave empty to match all logs. freshness: How far back to look, e.g. '30m', '1h', '2d'. Default '1h'. limit: Maximum number of entries to return (newest first). environment: Which configured GCP environment to query, e.g. 'staging' or 'production'. Omit to use the default environment. Call list_environments to see what is configured.get_recent_errorsReturn recent log entries at severity ERROR or higher. Args: service: Optional service name to narrow to (matched against resource.labels.service_name and logName). hours: How many hours back to search. Default 1. limit: Maximum number of entries to return (newest first). environment: Which configured GCP environment to query, e.g. 'staging' or 'production'. Omit to use the default environment.list_error_groupsList grouped application errors from Error Reporting with counts. Args: hours: Lookback window; snapped to the nearest supported period (1h, 6h, 1d, 1w, 30d). Default 24. service: Optional service name filter (Error Reporting "service" label). limit: Maximum number of error groups to return (most frequent first). environment: Which configured GCP environment to query, e.g. 'staging' or 'production'. Omit to use the default environment.query_metricQuery a Cloud Monitoring metric time series. Args: metric_type: Metric type, e.g. 'compute.googleapis.com/instance/cpu/utilization'. resource_filter: Optional extra filter, e.g. 'resource.labels.instance_id="123"'. window: How far back to query, e.g. '1h', '6h', '1d'. Default '1h'. aligner: Aggregation across the alignment period: MEAN, MAX, MIN, SUM, COUNT, RATE, PERCENTILE_99. Default MEAN. alignment_period: Bucket size for aggregation, e.g. '1m', '5m'. Default '5m'. limit: Maximum number of time series to return. environment: Which configured GCP environment to query, e.g. 'staging' or 'production'. Omit to use the default environment.list_alert_policiesList Cloud Monitoring alert policies and whether they are enabled. Args: limit: Maximum number of policies to return. environment: Which configured GCP environment to query, e.g. 'staging' or 'production'. Omit to use the default environment.list_uptime_checksList Cloud Monitoring uptime check configurations. Args: limit: Maximum number of uptime checks to return. environment: Which configured GCP environment to query, e.g. 'staging' or 'production'. Omit to use the default environment.list_cost_recommendationsAggregate GCP cost-optimization recommendations (idle/rightsizing/etc). Fans out the cost recommenders across the locations where the project has resources. Recommenders/locations that are empty or not enabled are skipped. Args: locations: Optional comma-separated zones/regions to scan (e.g. 'us-central1,us-central1-a'). If empty, auto-discovers from Asset Inventory (requires the Cloud Asset API). limit_per_call: Max recommendations to pull per recommender+location. environment: Which configured GCP environment to query, e.g. 'staging' or 'production'. Omit to use the default environment.list_recommendationsList recommendations from a specific recommender at a specific location. Args: recommender_id: Recommender id, e.g. 'google.compute.instance.MachineTypeRecommender' or 'google.iam.policy.Recommender'. location: Zone (e.g. 'us-central1-a'), region (e.g. 'us-central1'), or 'global' depending on the recommender. Default 'global'. limit: Maximum number of recommendations to return. environment: Which configured GCP environment to query, e.g. 'staging' or 'production'. Omit to use the default environment.get_cost_breakdownSummarize recent spend from the BigQuery billing export. By default this reports spend for the selected environment's project only. A billing export table covers every project on the billing account, and several environments commonly share one table, so an unfiltered query would return identical account-wide totals for staging and production. Only the environment holding the export needs 'billing_export_table' set: one billing account exports to one project, and that export already covers every project on the account. Asking any other environment for costs returns an error naming the one that has it. Args: group_by: One of 'service', 'sku', 'project', 'region'. Default 'service'. days: Lookback window in days over usage_start_time. Default 30. limit: Max rows returned (highest net cost first). all_projects: Report the whole billing account instead of just this environment's project. Pair with group_by='project' to compare projects; the totals then are not specific to this environment. environment: Which configured GCP environment to bill against, e.g. 'staging' or 'production'. Omit to use the default environment.get_billing_infoReturn the billing account linked to the project and its status. Args: environment: Which configured GCP environment to inspect, e.g. 'staging' or 'production'. Omit to use the default environment.search_assetsSearch all cloud resources in the project via Cloud Asset Inventory. Args: asset_types: Optional comma-separated asset types to filter, e.g. 'compute.googleapis.com/Instance,run.googleapis.com/Service'. query: Optional free-text/structured query, e.g. 'state:RUNNING' or 'location:us-central1'. limit: Maximum number of resources to return. Default 50; raise it when you know you need a full inventory, since each resource costs context. include_labels: Include resource labels. Off by default because deployment labels are usually the largest part of the response and rarely answer the question being asked. environment: Which configured GCP environment to search, e.g. 'staging' or 'production'. Omit to use the default environment. Call list_environments to see what is configured.- +4 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Alibaba Cloud Ops MCP Server
{
"mcpServers": {
"aliyun-alibaba-cloud-ops-mcp-server": {
"command": "uvx",
"args": [
"alibaba-cloud-ops-mcp-server@latest"
],
"env": {
"ALIBABA_CLOUD_ACCESS_KEY_ID": "YOUR_ALIBABA_CLOUD_ACCESS_KEY_ID_HERE",
"ALIBABA_CLOUD_ACCESS_KEY_SECRET": "YOUR_ALIBABA_CLOUD_ACCESS_KEY_SECRET_HERE"
}
}
}
}
GCP Platform Engineer
{
"mcpServers": {
"gcp-platform-engineer": {
"command": "uvx",
"args": [
"platform-mcp"
],
"env": {
"PLATFORM_MCP_CONFIG": "YOUR_PLATFORM_MCP_CONFIG_HERE",
"PLATFORM_MCP_ENVIRONMENTS": "YOUR_PLATFORM_MCP_ENVIRONMENTS_HERE",
"PLATFORM_MCP_DEFAULT_ENVIRONMENT": "YOUR_PLATFORM_MCP_DEFAULT_ENVIRONMENT_HERE",
"GOOGLE_APPLICATION_CREDENTIALS": "YOUR_GOOGLE_APPLICATION_CREDENTIALS_HERE"
}
}
}
}
Frequently asked questions
Alibaba Cloud Ops MCP Server is categorized under Cloud Platforms and uses a local stdio subprocess. GCP Platform Engineer belongs to Cloud Platforms and uses a local stdio subprocess. Pick Alibaba Cloud Ops MCP Server for cloud platforms work and GCP Platform Engineer for cloud platforms.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Alibaba Cloud Ops MCP Server is bring-your-own-key: usage is billed by the API provider you supply a key for. Auth: api key required. GCP Platform Engineer has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add aliyun-alibaba-cloud-ops-mcp-server and gcp-platform-engineer as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Alibaba Cloud Ops MCP Server has a quality signal of 65/100 (Great) with 10 views, 0 installs and 132 GitHub stars. GCP Platform Engineer has 59/100 (Good) with 2 views, 0 installs and 2 GitHub stars.