GCP Platform Engineer vs MCP Server Aws Sso
A side-by-side look at the GCP Platform Engineer and MCP Server Aws Sso MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose GCP Platform Engineer for cloud platforms work as a local process; choose MCP Server Aws Sso 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 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.
Choose MCP Server Aws Sso when
- You need dedicated capabilities in the Cloud Platforms domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: OAuth 2.0.
- You have the keys it needs:
AWS_SSO_START_URL, AWS_SSO_REGION, AWS_REGION. - You want tools like
aws_sso_login, aws_sso_status, aws_sso_ls_accounts.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Cloud Platforms | Cloud Platforms |
| Quality signal | 59/100 · Good | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | OAuth 2.0 |
| Pricing | Not declared | Free |
| Required env vars | PLATFORM_MCP_CONFIGPLATFORM_MCP_ENVIRONMENTSPLATFORM_MCP_DEFAULT_ENVIRONMENTGOOGLE_APPLICATION_CREDENTIALS | AWS_SSO_START_URLAWS_SSO_REGIONAWS_REGION |
| Verified owner | Community listing | Community listing |
| GitHub stars | 2 | 15 |
| Directory activity | 2 views 0 installs 0 upvotes | 7 views 0 installs 0 upvotes |
| Tools | 16 | 5 |
| Full listing | View GCP Platform Engineer | View MCP Server Aws Sso |
Tools & capabilities
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
MCP Server Aws Sso tools 5
aws_sso_loginInitiate AWS SSO device authorization flow to obtain temporary credentials. This flow works as follows: 1. Generates a unique user verification code and authentication URL 2. Opens a browser to AWS SSO login page (if `launchBrowser: true`) 3. You enter the verification code and complete AWS SSO login 4. Background polling automatically collects and caches the token 5. The cached token is used by other AWS SSO tools **IMPORTANT FOR AI ASSISTANTS**: When the tool returns authentication instructions: - ALWAYS check if a browser window opened automatically - If browser opened: Guide the user to complete authentication - If no browser opened: Instruct user to manually open the URL and enter code - Always provide both the verification code and URL as backup Prerequisites: - AWS SSO must be configured with a start URL and region - Browser access is required for authentication - You must have an AWS SSO account with appropriate permissions Returns: Authentication status, session details, verification code and URLaws_sso_statusCheck current AWS SSO authentication status. Verifies if a valid cached token exists and its expiration time. Does NOT perform authentication - only checks status. If no valid token exists, instructs you to run `aws_sso_login`. Use before calling `aws_sso_ls_accounts` or `aws_sso_exec_command`. Returns: Authentication status, session details, expiration time, next stepsaws_sso_ls_accountsList all AWS accounts and roles accessible through AWS SSO. Provides essential information needed for `aws_sso_exec_command`: - Fetches all accessible accounts with IDs, names, and emails - Retrieves all available roles for each account - Handles pagination internally - Caches account and role information Prerequisites: - MUST first authenticate using `aws_sso_login` - AWS SSO must be configured with a start URL and region Returns: Account list with IDs, names, roles, and session statusaws_sso_exec_commandExecute AWS CLI command using temporary credentials from AWS SSO. Workflow: 1. Verifies valid AWS SSO authentication token 2. Obtains temporary credentials for account and role 3. Executes the AWS CLI command 4. Caches credentials for future use (1 hour) Prerequisites: - MUST first authenticate using `aws_sso_login` - AWS CLI MUST be installed on the system - AWS SSO must be configured Required: `accountId`, `roleName`, `command` Optional: `region` Returns: Execution context, command output, errors, exit codeaws_sso_ec2_exec_commandExecute shell command on EC2 instance via SSM using AWS SSO credentials. No SSH access or inbound ports required. Uses SSM's RunShellScript document. Prerequisites: - MUST first authenticate using `aws_sso_login` - EC2 instance MUST have SSM Agent installed - Instance needs IAM role with AmazonSSMManagedInstanceCore policy - Your role needs `ssm:SendCommand` and `ssm:GetCommandInvocation` permissions Required: `instanceId`, `accountId`, `roleName`, `command` Optional: `region` Returns: Execution context, command output, errors, troubleshooting guidance
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.
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"
}
}
}
}
MCP Server Aws Sso
{
"mcpServers": {
"aashari-mcp-server-aws-sso": {
"command": "npx",
"args": [
"-y",
"@aashari/mcp-server-aws-sso"
],
"env": {
"AWS_SSO_START_URL": "YOUR_AWS_SSO_START_URL_HERE",
"AWS_SSO_REGION": "YOUR_AWS_SSO_REGION_HERE",
"AWS_REGION": "YOUR_AWS_REGION_HERE"
}
}
}
}
Frequently asked questions
GCP Platform Engineer is categorized under Cloud Platforms and uses a local stdio subprocess. MCP Server Aws Sso belongs to Cloud Platforms and uses a local stdio subprocess. Pick GCP Platform Engineer for cloud platforms work and MCP Server Aws Sso 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.
GCP Platform Engineer has no declared pricing — check the project's own site. MCP Server Aws Sso is listed as free. Auth: oauth 2.0. 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 gcp-platform-engineer and aashari-mcp-server-aws-sso as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, GCP Platform Engineer has a quality signal of 59/100 (Good) with 2 views, 0 installs and 2 GitHub stars. MCP Server Aws Sso has 63/100 (Good) with 7 views, 0 installs and 15 GitHub stars.