Slab MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Slab as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Slab. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Slab?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Slab tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Slab MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 12 tools from Slab
Why Use LlamaIndex with the Slab MCP Server
LlamaIndex provides unique advantages when paired with Slab through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Slab tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Slab tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Slab, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Slab tools were called, what data was returned, and how it influenced the final answer
Slab + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Slab MCP Server delivers measurable value.
Hybrid search: combine Slab real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Slab to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Slab for fresh data
Analytical workflows: chain Slab queries with LlamaIndex's data connectors to build multi-source analytical reports
Slab MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Slab to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Slab to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSlab + LlamaIndex FAQ
Common questions about integrating Slab MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Slab with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Slab to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.