How to Use the SleekFlow MCP in LlamaIndex

Q: How does LlamaIndex use SleekFlow for knowledge augmentation?

LlamaIndex takes the raw output of tools like listautomationflows and indexes them. This means you can ask natural language questions—like 'What are our automated flows?'—and get an answer sourced directly from the indexed API data.

LlamaIndex: Index all conversations and API results into a searchable knowledge base for better context.

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Connect SleekFlow MCP to LlamaIndex

Create your Vinkius account to connect SleekFlow to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup SleekFlow with LlamaIndex

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Knowledge-Grounded Conversation Retrieval

Instead of just executing, LlamaIndex indexes the results. After an agent runs `list_conversations`, that entire conversation log gets stored in your vector index. You can then query past interactions months later and get answers grounded in actual API data. This turns transient tool output into persistent knowledge. It's perfect for building support systems that need to reference history without having to run the tools again.

Indexing Contact Data

The `get_contact_details` tool doesn't just return data; it allows you to index specific customer profiles. When a user asks about 'John Smith,' your agent fetches his details, and that structured information becomes part of the knowledge base for future queries. This makes finding the right person or piece of info highly reliable. You query the stored facts, not just the live API call.

Semantic Search Across Channels

Want to know what was discussed on Instagram six months ago? Run `list_channels` first to confirm connectivity, then use `list_conversations`. The output is indexed, meaning you can search across all channels using natural language queries. It’s a massive upgrade from just running a command; it gives you semantic context and retrieval.

Setup guide

Set up SleekFlow MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all SleekFlow MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to SleekFlow tools.",
)
response = await agent.run("List recent SleekFlow data")

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Common questions about SleekFlow MCP in LlamaIndex

LlamaIndex takes the raw output of tools like `list_automation_flows` and indexes them. This means you can ask natural language questions—like 'What are our automated flows?'—and get an answer sourced directly from the indexed API data.

Yes, it's designed for that. You pass the tools to your FunctionAgent, and its output—whether it's a list of contacts or message logs—becomes part of the searchable knowledge base you build.

The conversation history is captured and indexed. This means the content of every message sent or received gets stored in your vector store alongside other relevant documents for future retrieval.

This server manages customer communications: contact details, conversation logs across WhatsApp and Instagram, and assigned labels. All this raw API output can be indexed by LlamaIndex.

It does significantly. Instead of relying only on the live tool call, you query your indexed knowledge base first. This is faster and gives much more reliable context than simple API calls alone.

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