google-cloud-aiplatform is a Google Cloud Python SDK package, but none of the candidate product pages in this set documents that SDK directly; the closest match is Google's own developer platform 6, whose listed actions include Google Cloud agent tooling. Please confirm which platform or task you need (e.g., Vertex AI model training, deployment, or agent development) so the answer can be narrowed.
A question back: Which platform or task do you mean by google-cloud-aiplatform: Vertex AI model training and deployment on Google Cloud, building Google Cloud AI agents, or something else?
- 1
Google Gemini Developer APIgoogle.comverified and signed
Google's AI developer platform offering Gemini and Gemma models through the Gemini API and Google AI Studio 6, with agent tooling for Google Cloud such as agents-cli workflow and deployment actions 16.
official:CLI · googletestskill · google-agents-cli-deployskill · google-agents-cli-publishskill · google-agents-cli-workflow+1,146 community tools built for it
for agents: google-cloud-solution-agentic-ai-data-science-workflow · google-cloud-solution-agentic-ai-bidirectional-streaming · google-agents-cli-onboarding
- 2
Microsoft Foundrymicrosoft.comverified and signed
Microsoft's agent and model platform, with a skill for building, deploying, and managing Foundry agents and models end to end 14.
official:library · Microsoft.OpenApiskill · microsoft-foundryskill · appinsights-instrumentation+136 community tools built for it
for agents: hosted-agents-v2-py · airunway-aks-setup · microsoft-foundry
Sources
- 4Microsoft – AI, Cloud, Productivity, Computing, Gaming & Appsmicrosoft.com · read 2026-10-29
- 6Gemini Developer API | Gemma open models | Google AI for Developersai.google.dev · read 2026-10-29
- 14hosted-agents-v2-py: declares "hosted-agents-v2-py" (the record we read and signed)unfragile.ai · read 2026-10-24
- 16google-cloud-solution-agentic-ai-data-science-workflow: declares "google-cloud-solution-agentic-ai-data-science-workflow" (the record we read and signed)unfragile.ai · read 2026-10-24
For agents: the 10 actions behind this answer
python-mcp-server-generator · python-mcp-server-generator verified and signed
Use this skill when the user wants to create a new Model Context Protocol (MCP) server in Python. Also use it to expose Python functions, an API, or a database to AI clients as MCP tools. The skill makes a uv project with the MCP Python SDK v2 (MCPServer), typed tools, in-memory tests, and client co
sentry-python-sdk · sentry-python-sdk verified and signed
Full Sentry SDK setup for Python. Use when asked to "add Sentry to Python", "install sentry-sdk", "setup Sentry in Python", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for Python applications. Supports Django, Flask, FastAPI, Celery, Starlette, AIOHTT
databricks-jobs · databricks-jobs verified and signed
Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.
hosted-agents-v2-py · hosted-agents-v2-py verified and signed
Build hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating container-based agents that run custom code in Azure AI Foundry. Triggers: "ImageBasedHostedAgentDefinition", "hosted agent", "container agent", "create_version", "ProtocolVersionRecord", "AgentP
Execute Python in a sandbox · Pydantic verified and signed
Run Python code in a secure sandbox with access to Logfire platform helper functions for advanced analysis.
google-cloud-solution-agentic-ai-data-science-workflow · google-cloud-solution-agentic-ai-data-science-workflow verified and signed
Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, o
upstash-box-py · upstash-box-py verified and signed
Work with the upstash-box Python SDK for sandboxed cloud containers with AI agents, shell, filesystem, git, cron schedules, snapshots, and a headless browser. Use when building with Upstash Box in Python, creating a sandbox or isolated environment to run untrusted or agent-generated code, running AI
chatgpt-apps · chatgpt-apps verified and signed
Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a doc
run_code · e2b-mcp-server found in code, not yet checked
Run python code in a secure sandbox by E2B. Using the Jupyter Notebook syntax.
modal · modal verified and signed
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.