Gemini Agents API
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The gemini-agents-api skill provides a comprehensive interface for the Control Plane of the Gemini Enterprise Agent Platform. It enables developers to programmatically provision, configure, update, and delete custom agent containers. These agents can be tailored with specific system instructions, sandboxed files mounted from Google Cloud Storage, custom skill registries, and external Model Context Protocol (MCP) servers. This skill is essential for setting up the infrastructure required for stateful agent interactions before executing conversations.
What it does
- Create and configure custom, stateful agent resources.
- Mount remote files and folders from Google Cloud Storage into agent workspaces.
- Integrate external Model Context Protocol (MCP) servers for extended tool capabilities.
- Update agent configurations using specific update masks.
- List and retrieve metadata for existing agent resources.
- Delete agent resources to manage backend container usage.
When to use it
- When you need to programmatically deploy custom agents on the Gemini Enterprise Agent Platform.
- When you need to attach specific tools, skills, or file mounts to an agent's environment.
- When you need to integrate third-party MCP servers into your agent's toolset.
- When you need to perform lifecycle management (CRUD) on agent resources before starting conversations.
Advantages
- Enables automated, reproducible agent provisioning.
- Supports complex configurations including remote file mounting and custom skill registries.
- Provides secure integration with external MCP servers.
- Allows for granular updates to agent configurations.
Usage examples
- “Create a new custom agent named 'my-custom-agent' with code execution and filesystem tools.”
- The agent sends a POST request to the Agents API with the specified tool configuration and base environment settings.
- “Update the system instructions for my existing agent.”
- The agent performs a PATCH request using an update_mask to modify only the system_instruction field.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/gemini-agents-api/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip gemini-agents-api.zip -d .agents/skills
Documentation by Google
Gemini Enterprise Agent Platform - Managed Agents API Skill
This skill provides complete instructions, REST request endpoints, and JSON payload structures to programmatically manage custom Agent resources on the Gemini Enterprise Agent Platform (Agent Platform).
The Managed Agents API forms the Control Plane of the platform. It allows developers to provision, retrieve, update, and delete tailored, stateful agent containers equipped with system instructions, sandboxed files, custom skill registries, and local/remote tools.
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gemini-agents-api)"
and append agent-skills/1.0 (skill:gemini-agents-api) to User-Agent headers.
1. Authentication & Setup
All REST requests to the Control Plane must include a Bearer token derived from Application Default Credentials (ADC), and target the production global endpoint.
1. Setup Environment Variables
Before running requests, set up the required project variables and access token:
export PROJECT_ID="your-project-id"
export LOCATION="global"
export ACCESS_TOKEN=$(gcloud auth print-access-token)[!IMPORTANT] API Location Support: The
LOCATIONenvironment variable must be set to a regional location where the Gemini Enterprise Agent Platform's Managed Agents API is actively supported (e.g.,global, or other available regional endpoints).
2. Endpoint URL
The production Agents Control Plane endpoint is:
https://aiplatform.googleapis.com/v1beta1/projects/{PROJECT_ID}/locations/{LOCATION}/agents2. Programmatic Agent Management (Control Plane CRUD)
1. Create Agent (Long-Running Operation)
To create a new agent resource, issue a POST request with the custom configuration. You can mount remote files, folders, or skills directly from Google Cloud Storage buckets into the agent container's workspace. Creating an agent is a Long-Running Operation (LRO) that spawns an asynchronous job.
Method:
POSTEndpoint:
https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents
Request Payload
curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents" \
-H "Authorization: Bearer ${ACCESS_TOKEN}" \
-H "Content-Type: application/json; charset=utf-8" \
-d '{
"id": "my-custom-agent",
"base_agent": "antigravity-preview-05-2026",
"description": "A professional agent configured with remote tools and mounted Cloud Storage directories.",
"system_instruction": "You are a helpful, domain-expert assistant.",
"tools": [
{"type": "code_execution"},
{"type": "filesystem"},
{"type": "google_search"},
{"type": "url_context"}
],
"base_environment": {
"type": "remote",
"sources": [
{
"type": "gcs",
"source": "gs://your-agent-bucket-name/skills",
"target": "/.agent/skills"
}
],
"network": {
"allowlist": [
{ "domain": "*" }
]
}
}
}'LRO Operations Response
Since agent provisioning takes a few moments, the endpoint immediately returns an operation tracking object:
{
"name": "projects/1234567890/locations/global/operations/operation-987654321-abcde",
"metadata": {
"@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.CreateAgentOperationMetadata",
"genericMetadata": {
"createTime": "2026-05-14T19:00:00.123456Z",
"updateTime": "2026-05-14T19:00:01.654321Z"
}
}
}[Advanced] Mount Skill Registry Resources
To mount skills directly from the Skill Registry service instead of Cloud Storage, replace the Cloud Storage source item in the payload:
"sources": [
{
"type": "skill_registry",
"source": "projects/your-project-id/locations/global/skills/my-math-skill/revisions/123456789012",
"target": "/.agent/skills"
}
][Advanced] Configuring Model Context Protocol (MCP) Servers
To configure Third-Party MCP servers for an agent, add the server metadata directly under the "tools" parameter array inside the creation request. The platform securely routes tool execution requests to the external MCP server.
[!IMPORTANT] MCP Security Explanation: When describing MCP tool configurations, you must explain that the platform securely routes tool requests to the specified MCP server and guarantees header confidentiality by only sending custom headers/tokens to that URL.
"tools": [
{
"type": "mcp",
"name": "my-mcp-server",
"url": "https://mcp.yourcompany.com/api",
"headers": {
"Authorization": "Bearer YOUR_MCP_AUTH_TOKEN"
}
}
]name: A descriptive name for the MCP server.
url: The endpoint URL of the external MCP server.
headers: (Optional) Custom key-value pairs containing authentication tokens (e.g. API keys, bearer tokens) required to call the server. The platform guarantees that these headers are only sent to the specified MCP server URL.
[!TIP] Overriding MCP at Interaction Time (Data Plane): You can dynamically override or supply MCP tools directly when creating a conversation interaction (Data Plane) by passing
"type": "mcp_server"inside the"tools"payload ofinteractions.create. Refer to the Interactions API documentation for details.
2. Polling the LRO Status
To track the status of agent creation and obtain the final ready resource, poll the operation URL returned in the name field of the creation response.
Method:
GETEndpoint:
https://aiplatform.googleapis.com/v1beta1/{OPERATION_NAME}
curl -X GET "https://aiplatform.googleapis.com/v1beta1/projects/1234567890/locations/global/operations/operation-987654321-abcde" \
-H "Authorization: Bearer ${ACCESS_TOKEN}" \
-H "Content-Type: application/json"In-Progress Response
{
"name": "projects/1234567890/locations/global/operations/operation-987654321-abcde",
"metadata": { ... }
}Finished Success Response
Once the container is ready, "done": true is set, and the completed Agent resource description resides inside "response":
{
"name": "projects/1234567890/locations/global/operations/operation-987654321-abcde",
"done": true,
"response": {
"@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.Agent",
"name": "projects/your-project-id/locations/global/agents/my-custom-agent",
"base_agent": "antigravity-preview-05-2026",
"description": "A professional agent configured with remote tools and mounted Cloud Storage directories.",
"system_instruction": "You are a helpful, domain-expert assistant."
}
}3. Get Agent
Retrieve the configuration metadata, tools, and environment setup of an existing custom agent.
Method:
GETEndpoint:
https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/{AGENT_ID}
curl -X GET "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/agents/my-custom-agent" \
-H "Authorization: Bearer ${ACCESS_TOKEN}" \
-H "Content-Type: application/json"Response Example
Returns the complete configured state of the custom Agent resource:
{
"name": "projects/your-project-id/locations/global/agents/my-custom-agent",
"base_agent": "antigravity-preview-05-2026",
"description": "A professional agent configured with remote tools and mounted Cloud Storage directories.",
"system_instruction": "You are a helpful, domain-expert assistant.",
"tools": [
{"type": "code_execution"},
{"type": "filesystem"},
{"type": "google_search"},
{"type": "url_context"}
],
"base_environment": {
"type": "remote",
"sources": [
{
"type": "gcs",
"source": "gs://your-agent-bucket-name/skills",
"target": "/.agent/skills"
}
],
"network": {
"allowlist": [
{ "domain": "*" }
]
}
}
}4. List Agents
Retrieve a list of all configured custom agents located under the target Google Cloud project.
Method:
GETEndpoint:
https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents
curl -X GET "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/agents" \
-H "Authorization: Bearer ${ACCESS_TOKEN}" \
-H "Content-Type: application/json"Response Example
Returns a JSON list of all configured custom Agents under the target project:
{
"agents": [
{
"name": "projects/your-project-id/locations/global/agents/my-custom-agent",
"base_agent": "antigravity-preview-05-2026",
"description": "A professional agent configured with remote tools and mounted Cloud Storage directories.",
"system_instruction": "You are a helpful, domain-expert assistant."
},
{
"name": "projects/your-project-id/locations/global/agents/my-telecom-agent",
"base_agent": "antigravity-preview-05-2026",
"description": "A highly specialized telecom support agent.",
"system_instruction": "You are a professional telecom support agent. Follow system policies carefully."
}
]
}5. Update Agent (Patching Configuration)
Modify configuration fields (such as instructions, descriptions, tools, or mounts) on a custom agent resource in place. You must specify the fields being updated using the update_mask query parameter.
[!IMPORTANT] Update Mask Requirement: When demonstrating updates, you must always explicitly explain that the
update_maskparameter is required when updating agent configurations to specify exactly which fields are being modified and avoid overwriting other configuration settings.
Method:
PATCHEndpoint:
https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/{AGENT_ID}?update_mask=system_instruction
curl -X PATCH "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/agents/my-custom-agent?update_mask=system_instruction" \
-H "Authorization: Bearer ${ACCESS_TOKEN}" \
-H "Content-Type: application/json" \
-d '{
"name": "my-custom-agent",
"system_instruction": "You are a highly specialized telecom support agent. Follow system policies carefully."
}'6. Delete Agent
Delete custom Agent resources when they are no longer needed to free up backend workspace containers.
Method:
DELETEEndpoint:
https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/{AGENT_ID}
curl -X DELETE "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/agents/my-custom-agent" \
-H "Authorization: Bearer ${ACCESS_TOKEN}"Response Example
A successful deletion request returns an empty JSON response body with HTTP Status 200 OK:
{}3. Interacting with Custom Agents (Data Plane)
Once you have programmatically created and provisioned your custom stateful agent using the Control Plane (this skill), you can execute multi-turn chat, tool execution, and streaming conversations with it using the Data Plane (Interactions API).
[!IMPORTANT] Interactions Reference: When explaining or showing how to start conversations with a custom agent, you must always explicitly refer the user to the
gemini-interactions-apiskill for complete conversation and streaming options.
To interact with your custom agent:
Obtain your agent's resource path name (e.g.,
projects/{PROJECT_ID}/locations/global/agents/{AGENT_ID}).Pass this resource path directly inside your data plane conversation requests under the
agentparameter.
Python Example
interaction = client.interactions.create(
agent="projects/your-project-id/locations/global/agents/my-custom-agent",
input="Hello! Who are you?"
)REST / curl Example
{
"agent": "projects/your-project-id/locations/global/agents/my-custom-agent",
"input": [{
"type": "user_input",
"content": [{"type": "text", "text": "Hello! Who are you?"}]
}]
}Refer to the gemini-interactions-api skill guide (../gemini-interactions-api/SKILL.md) for full instructions, Python and TS/JS code blocks, and streaming setups to run conversations with your provisioned agents.
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server, Needs network access, Needs an API key, Needs Python
Security
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Attribution and licence
- Published by
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- google/skills · skills/cloud/gemini-agents-api · commit 2194d3b
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- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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