Can my AI use existing wiki guidelines to write new code or copy?

Absolutely. You can request your agent to 'search the Slab wiki for our Frontend Coding Standards' or 'find our Brand Voice Guidelines'. The agent will retrieve the exact Markdown content of those articles and use them as system instructions for the rest of your conversation.

How do I easily publish my AI chat output back to Slab?

When your AI agent generates a good technical specification or summary, simply tell it: 'Create a new post in Slab called [Name], using this entire response as the content, and place it in the Engineering topic.' The agent will format the Markdown and publish it immediately through the `create_post` tool.

Can my agent clean up outdated company documentation?

Yes. If an article is deprecated, you can tell your AI: 'Archive the post with ID XYZ' or 'Find the old setup guide and archive it.' The agent can execute the `archive_post` command to hide outdated information and keep your knowledge base pristine.

Slab MCP Server for Google ADK 12 tools — connect in under 2 minutes

Name: Slab
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 12 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Slab as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="slab_agent",
    instruction=(
        "You help users interact with Slab "
        "using 12 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Slab MCP Server

Connect your Slab workspace to any AI agent and empower your team to search, read, and write documentation seamlessly. Interact with your organization's entire knowledge base through natural language without ever switching tabs.

Google ADK natively supports Slab as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 12 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Deep Search & Retrieval — Execute full-text searches across all Slab posts to fetch answers, guidelines, and protocols instantly
Documentation Authoring — Create new articles, meeting notes, or project specs in Markdown, and update existing posts on the fly
Information Architecture — Browse all your topics (folders) to understand how the company wiki is structured and fetch categorized articles
Activity Feeds — Pull the most recently updated posts to stay on top of new company policies and documentation changes
Team Discovery — Retrieve organization metadata and list all registered team members

The Slab MCP Server exposes 12 tools through the Vinkius. Connect it to Google ADK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Slab to Google ADK via MCP

Follow these steps to integrate the Slab MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 12 tools from Slab via MCP

Why Use Google ADK with the Slab MCP Server

Google ADK provides unique advantages when paired with Slab through the Model Context Protocol.

Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Slab

Production-ready features like session management, evaluation, and deployment come built-in. not bolted on

Seamless integration with Google Cloud services means you can combine Slab tools with BigQuery, Vertex AI, and Cloud Functions

Slab + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Slab MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Slab and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Slab tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Slab regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Slab

Slab MCP Tools for Google ADK (12)

These 12 tools become available when you connect Slab to Google ADK via MCP:

archive_post

This action is irreversible via API. Archive an existing Slab post

create_post

Provide content in Markdown. Create a new wiki post in Slab

create_topic

Create a new topic in Slab to organize posts

get_organization

Retrieve the Slab organization profile

get_post_details

Retrieve the full content and metadata of a specific Slab post

get_topic_details

Retrieve details and list of posts for a specific Slab topic

list_posts

Returns post IDs and titles. List all wiki posts/articles in the Slab workspace

list_recent_posts

List the most recently updated posts

list_topics

List all topics organizing posts in the Slab workspace

list_users

List all members of the Slab organization

search_posts

Full-text search across all Slab posts

update_post

Update an existing Slab post title or content

Example Prompts for Slab in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with Slab immediately.

"Search the Slab wiki for 'VPN Setup Instructions'."

"Create a new topic named 'Q3 Planning' and list the ID so I can save posts to it."

"List the most recent 5 posts updated in the company wiki."

Troubleshooting Slab MCP Server with Google ADK

Common issues when connecting Slab to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Slab + Google ADK FAQ

Common questions about integrating Slab MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.