---

name: tools-and-integrations description: "Bind and troubleshoot ADK Python tools, ToolContext, long-running and confirmation tools, auth, MCP/OpenAPI/Google API/cloud integrations, A2A helpers, and optional extras." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ADK Tools and Integrations

Use this sub-skill when a user asks to add, wrap, authenticate, expose, or debug tools and integration toolsets in an ADK Python agent.

Route Here

• Bind Python functions with FunctionTool, LongRunningFunctionTool, ToolContext, request_input, or explicit tool_context.request_confirmation().
• Build custom BaseTool or BaseToolset classes, filter/prefix toolsets, or close toolset resources safely.
• Wrap agents as tools with AgentTool, delegate with TransferToAgentTool or transfer_to_agent, or diagnose tool-name routing issues.
• Add auth with AuthConfig, AuthCredential, AuthenticatedFunctionTool, OpenAPI auth helpers, MCP auth, or credential request flows.
• Configure MCP, OpenAPI, API Hub, Google API, Application Integration, BigQuery/Bigtable/Spanner/Pub/Sub/Data Agent/Toolbox/Slack/A2A integrations.
• Diagnose missing optional extras such as google-adk[mcp], google-adk[extensions], google-adk[gcp], google-adk[tools], google-adk[a2a], google-adk[toolbox], or google-adk[db].

Route Elsewhere

• Agent constructor field placement, model, instruction, schemas, and callback basics: use agent-construction.
• CLI command usage, app discovery, local servers, deployment, and YAML config loading: use cli-configuration-deployment.
• Runner, App, session/memory/artifact persistence, plugins, telemetry, and service lifecycles: use runtime-services.
• Workflow graph nodes, Workflow, HITL node resume, joins, and dynamic graph routing: use workflow-orchestration.
• Modifying ADK repository source, style, tests, docs, or samples: use repo-development.

Quick Start

1. Prefer bare callables for simple tools; wrap with FunctionTool(func=..., require_confirmation=...) only when you need confirmation or explicit tool object behavior.
2. Add tool_context: ToolContext only when the tool needs state, artifacts, credentials, confirmation, transfer actions, or workflow node helpers; the parameter must be named tool_context.
3. Return JSON-serializable dictionaries for structured tool results; return {"error": "..."} when the model should see a recoverable tool failure.
4. Use LongRunningFunctionTool for work that returns pending status or pauses for external input; do not retry the same long-running call unless the returned event requests it.
5. Treat MCP, cloud, database, and A2A helpers as optional-extra surfaces; first run the bundled inspection script before assuming an import is available.

from google.adk import Agent
from google.adk.tools import FunctionTool, ToolContext


def transfer_funds(amount: float, recipient: str, tool_context: ToolContext):
  """Transfer funds after ADK collects confirmation."""
  return {"status": "scheduled", "amount": amount, "recipient": recipient}


root_agent = Agent(
    name="finance_agent",
    model="gemini-3.5-flash",
    instruction="Use tools for account operations and explain the result.",
    tools=[FunctionTool(func=transfer_funds, require_confirmation=True)],
)

References

• API reference — public classes, constructors, auth models, tool context methods, integration toolset families, optional extras, and local validation checks.
• Workflows — recipes for function tools, confirmation, long-running tools, MCP, OpenAPI, Google API/cloud integrations, A2A exposure, and safe fallback planning.
• Troubleshooting — fixes for missing extras, credentials, auth config, schema conversion, MCP sessions, cloud clients, confirmation confusion, and swallowed tool errors.
• Inspection script — no-network diagnostic that prints installed tool signatures and reports optional-extra availability without failing the whole run.

Tooling Checklist

• Tool names are stable, unique, and derived from clear function/class names; descriptions come from docstrings or explicit BaseTool descriptions.
• Function parameters are typed with JSON-schema-friendly types; Pydantic models are accepted for structured arguments but should be validated before side effects.
• ToolContext is used for state, credentials, confirmation, memory/artifacts, transfer actions, or node execution; it is not exposed to the model as a normal argument.
• Sensitive operations require either require_confirmation=True or explicit tool_context.request_confirmation() handling.
• Integration code documents which extra and which credential source are required, and includes a local import/help probe before any network or credential-dependent call.
• Tool exceptions are either intentionally propagated or translated by on_tool_error_callback; recoverable domain failures return a function response such as {"error": "..."}.