Tools And Integrations logo

Tools And Integrations

Organization
VectorSpaceLab
tools-and-integrations

Bind and troubleshoot ADK Python tools, ToolContext, long-running and confirmation tools, auth, MCP/OpenAPI/Google API/cloud integrations, A2A helpers, and optional extras.

Overview

PublisherVectorSpaceLab
RepositoryAREX-Skill
Skill nametools-and-integrations
Stars
341
Forks
26
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Tools And Integrations 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.
    https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adk-python/sub-skills/tools-and-integrations
  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/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/skills/repositories/repo-skills/adk-python/sub-skills/tools-and-integrations .claude/skills/tools-and-integrations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tools And Integrations 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 Tools And Integrations 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 Tools And Integrations 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.

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.
python
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": "..."}.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tools And Integrations AI skill do?

Bind and troubleshoot ADK Python tools, ToolContext, long-running and confirmation tools, auth, MCP/OpenAPI/Google API/cloud integrations, A2A helpers, and optional extras.

Why use Tools And Integrations on TypingMind?

Because you install it once and use it with any model. Tools And Integrations 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 Tools And Integrations in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adk-python/sub-skills/tools-and-integrations. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tools And Integrations?

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 Tools And Integrations?

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

Is the Tools And Integrations AI skill free?

Yes. It is published on GitHub by VectorSpaceLab 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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