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Google Agents Cli Publish

OrganizationPopular
google
google-agents-cli-publish

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini Enterprise", "publish to Gemini Enterprise", or needs guidance on the agents-cli publish gemini-enterprise command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

Overview

Publishergoogle
Repositoryagents-cli
Skill namegoogle-agents-cli-publish
Stars
6K
Forks
664
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

Install the Google Agents Cli Publish AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google/agents-cli.git /tmp/agents-cli
mkdir -p .claude/skills
cp -r /tmp/agents-cli/skills/google-agents-cli-publish .claude/skills/google-agents-cli-publish
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Google Agents Cli Publish in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Google Agents Cli Publish on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Google Agents Cli Publish is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Gemini Enterprise Registration

Requires: A deployed agent. For Agent Runtime, deployment_metadata.json (created by agents-cli deploy) enables auto-detection. For Cloud Run or GKE, provide the agent card URL and flags directly.

Prerequisites

  1. Agent must be deployed — the agent must be running and reachable
  2. Gemini Enterprise app must exist — Create one in Google Cloud Console → Gemini Enterprise → Apps before registering
  3. deployment_metadata.json (Agent Runtime only) — Created automatically by agents-cli deploy; contains the agent runtime ID, deployment target, the A2A flag, and the agent directory

Required Permissions for A2A on Cloud Run

  • roles/run.servicesInvoker granted to the Discovery Engine service account (service-<PROJECT_NUMBER>@gcp-sa-discoveryengine.iam.gserviceaccount.com) on the Cloud Run service.

Registration Modes

A2A Registration

Every scaffolded agent serves the Agent-to-Agent protocol. A2A is the default — and only — registration type on Cloud Run and GKE (no reasoning engine to invoke natively). It also works on Agent Runtime via --registration-type a2a. For an ADK agent there the CLI warns against it, because Gemini Enterprise can invoke Agent Runtime natively via :streamQuery — prefer ADK registration in that case. For an agent built on another framework there is no ADK app to invoke natively, so A2A is the right mode on every target and the warning is expected. Pass the agent card URL and the command fetches the card and registers it; display name and description default to the card's name/description.

bash
# A2A on Cloud Run / GKE. The card path depends on the project's language:
#   Python -> /a2a/{app_name}/.well-known/agent-card.json
#   Go     -> /.well-known/agent-card.json
agents-cli publish gemini-enterprise \
  --agent-card-url https://my-service-abc123.us-east1.run.app/a2a/app/.well-known/agent-card.json \
  --gemini-enterprise-app-id projects/123456/locations/global/collections/default_collection/engines/my-app

Pass --display-name / --description to override the card defaults. On Agent Runtime, the card URL auto-builds from deployment_metadata.json if you omit --agent-card-url.

ADK Registration (default on Agent Runtime)

ADK projects only. The agent must be deployed to Agent Runtime as an ADK app, since registration invokes it through :streamQuery. An agent on another framework registers over A2A, so deploy it to Cloud Run or GKE and publish from there.

This is the default and recommended registration for ADK agents on Agent Runtime: Gemini Enterprise invokes the agent natively via :streamQuery on its reasoning engine resource, authenticating end-to-end. Under the hood, :streamQuery dispatches to the AdkApp's streaming_agent_run_with_events method — when debugging an ADK invocation, search the runtime's reasoning_engine_stderr logs for that method name to trace the failure. It's also the path to use when the agent needs an OAuth authorization (--authorization-id). The agent is registered directly via its reasoning engine resource name; no agent card URL is needed.

bash
agents-cli publish gemini-enterprise \
  --registration-type adk \
  --agent-runtime-id projects/123456/locations/us-east1/reasoningEngines/789 \
  --gemini-enterprise-app-id projects/123456/locations/global/collections/default_collection/engines/my-app \
  --display-name "My Agent" \
  --description "Handles customer queries" \
  --tool-description "Answers questions about products"

Programmatic Mode (CI/CD)

The command is non-interactive by default — pass all required values via flags or environment variables. This makes it safe for CI/CD pipelines.

Via flags

bash
agents-cli publish gemini-enterprise \
  --agent-runtime-id "$AGENT_RUNTIME_ID" \
  --gemini-enterprise-app-id "$GEMINI_ENTERPRISE_APP_ID" \
  --display-name "Production Agent" \
  --registration-type adk

Via environment variables

Most flags have an env var alternative (--metadata-file, --interactive, and --list do not):

bash
export AGENT_RUNTIME_ID="projects/123456/locations/us-east1/reasoningEngines/789"
export GEMINI_ENTERPRISE_APP_ID="projects/123456/locations/global/collections/default_collection/engines/my-app"
export GEMINI_DISPLAY_NAME="Production Agent"
export GEMINI_DESCRIPTION="Handles customer queries"

agents-cli publish gemini-enterprise

Interactive Mode (--interactive)

Pass --interactive (or -i) to be guided through any missing values with interactive prompts. The command will list available Gemini Enterprise apps, offer to auto-detect the agent runtime ID from metadata, and prompt for display name and description.

bash
agents-cli publish gemini-enterprise --interactive

Complete Flag Reference

FlagEnv VarDescription
--agent-runtime-idAGENT_RUNTIME_IDAgent Runtime resource name (auto-detected from deployment_metadata.json)
--gemini-enterprise-app-idID or GEMINI_ENTERPRISE_APP_IDGemini Enterprise app full resource name
--display-nameGEMINI_DISPLAY_NAMEDisplay name in Gemini Enterprise
--descriptionGEMINI_DESCRIPTIONAgent description
--tool-descriptionGEMINI_TOOL_DESCRIPTIONTool description (ADK mode only, defaults to description)
--registration-typeREGISTRATION_TYPEadk or a2a (defaults to adk for an ADK agent on Agent Runtime, a2a everywhere else, including any non-ADK framework)
--agent-card-urlAGENT_CARD_URLAgent card URL for A2A registration
--deployment-targetDEPLOYMENT_TARGETagent_runtime, cloud_run, or gke (sets the default registration type — ADK on Agent Runtime, A2A on Cloud Run / GKE — and the A2A auth method)
--project-idGOOGLE_CLOUD_PROJECTGCP project ID for billing
--project-numberPROJECT_NUMBERGCP project number (used for Gemini Enterprise lookup)
--authorization-idGEMINI_AUTHORIZATION_IDOAuth authorization resource name
--metadata-filePath to deployment metadata (default: deployment_metadata.json)
--interactive / -iEnable interactive prompts
--listList Gemini Enterprise apps in the current project and exit

Auto-Detection from Metadata

When deployment_metadata.json exists, the command automatically:

  • Reads the agent runtime ID (remote_agent_runtime_id)
  • Determines the registration type: defaults to ADK (native :streamQuery) on Agent Runtime, and A2A on Cloud Run / GKE (which have no reasoning engine). A project scaffolded with another framework serves no ADK app, so it defaults to A2A on every target. Override with --registration-type.
  • Determines the deployment target for authentication

This means that for the simplest case (an ADK agent on Agent Runtime, registered as ADK), you only need to provide the Gemini Enterprise app ID:

bash
agents-cli publish gemini-enterprise \
  --gemini-enterprise-app-id projects/123456/locations/global/collections/default_collection/engines/my-app

SDK Compatibility (Python only)

Agent Runtime deployments may encounter "Session not found" errors with google-cloud-aiplatform versions <= 1.128.0. In interactive mode (--interactive), the command checks the SDK version from uv.lock and offers to upgrade. In programmatic mode, ensure your SDK is up to date before registering.


Agent Registry (agents and MCP servers)

Agent Registry (Preview) is the Google Cloud fleet-wide catalog of agents and MCP servers, separate from a Gemini Enterprise app. Agents deployed to a managed runtime (Agent Runtime on Gemini Enterprise Agent Platform) are auto-registered — no extra step after agents-cli deploy. Manage them with gcloud (requires roles/agentregistry.editor):

bash
# List / inspect agents
gcloud agent-registry agents list --project PROJECT --location LOCATION
gcloud agent-registry agents describe AGENT_NAME

# Update endpoint/metadata — edit the Service resource, not the Agent
gcloud agent-registry services update AGENT_NAME \
  --display-name "..." --description "..." \
  --interfaces "url=ENDPOINT_URL,protocolBinding=http-json"

# Register an external MCP server: not auto-introspected, so upload a
# toolspec.json (its tools/list response, max 10 KB). No us/eu multi-region.
gcloud agent-registry services create SERVER_NAME --location=LOCATION \
  --mcp-server-spec-type=tool-spec --mcp-server-spec-content=toolspec.json \
  --interfaces="url=SERVER_URL,protocolBinding=jsonrpc"  # or http-json, grpc

# Remove: delete the underlying runtime agent (auto-registered) OR, for
# manually registered agents/servers, delete the Service resource
gcloud agent-registry services delete NAME

Terraform: google_agent_registry_service with an mcp_server_spec block.

Docs: https://docs.cloud.google.com/agent-registry/manage-agents · https://docs.cloud.google.com/agent-registry/register-mcp-servers


Troubleshooting

IssueSolution
"Session not found" after registrationSDK version issue — upgrade google-cloud-aiplatform (see SDK Compatibility above), redeploy, then re-register
--registration-type is requiredNon-interactive mode needs --registration-type when no deployment_metadata.json exists
"Gemini Enterprise App ID is required"Provide --gemini-enterprise-app-id or set the ID / GEMINI_ENTERPRISE_APP_ID env var
Re-publishing the same agentRegistration is idempotent — re-running updates the existing registration in place instead of creating a duplicate
HTTP 403 on registrationCheck that your account has Discovery Engine Editor permissions on the Gemini Enterprise project
Debugging ADK invocation failures on Agent RuntimeGemini Enterprise calls the agent via the AdkApp's streaming_agent_run_with_events method (the native :streamQuery contract). Grep the runtime's reasoning_engine_stderr logs for streaming_agent_run_with_events to find the underlying error
"Could not fetch agent card"Verify the agent is running and the URL is correct; for Cloud Run, ensure gcloud auth login is done

Related Skills

  • /google-agents-cli-deploy — Deployment targets, CI/CD pipelines, and production workflows (also covers Agent Gateway governed ingress/egress and Semantic Governance awareness)
  • /google-agents-cli-workflow — Development workflow, coding guidelines, and operational rules
  • /google-agents-cli-scaffold — Project creation and enhancement with agents-cli scaffold create / scaffold enhance

Frequently asked questions

What does the Google Agents Cli Publish AI skill do?

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini Enterprise", "publish to Gemini Enterprise", or needs guidance on the agents-cli publish gemini-enterprise command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite....

Why use Google Agents Cli Publish on TypingMind?

Because you install it once and use it with any model. Google Agents Cli Publish is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Google Agents Cli Publish in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/agents-cli/tree/main/skills/google-agents-cli-publish. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Google Agents Cli Publish?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Google Agents Cli Publish?

As many as you like. As long as a model supports skills, you can use Google Agents Cli Publish with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Google Agents Cli Publish AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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