How to Use the ChatFly MCP in LlamaIndex

Q: How does ChatFly work with LlamaIndex RAG?

You use the ChatFly tools like listbots and getbot to fetch the current state of your chatbots. LlamaIndex then indexes this API data, letting your RAG application answer questions about your bot configurations with up-to-date information.

Turn your ChatFly bot configurations into a queryable knowledge base with LlamaIndex.

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Connect ChatFly MCP to LlamaIndex

Create your Vinkius account to connect ChatFly to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ChatFly with LlamaIndex

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Index Your Chatbot Configurations

The `list_bots` tool is your starting point. Use it to pull a complete roster of your deployed chatbots. An agent can then loop this list and use `get_bot` on each one to fetch its exact setup. LlamaIndex turns this raw API output into a structured, queryable index. Now you can ask your RAG application questions in plain English, like "show me all bots with 'sales' in their name," and get answers pulled directly from your ChatFly account.

Query Your Bot's Knowledge

The `list_data_sources` tool gets you the file names or URLs that a specific bot uses for knowledge. It tells you what a bot knows. By indexing this data with LlamaIndex, you build a system that answers questions about your bots' training. Ask "Which bots are trained on the Q3 pricing PDF?" and get an immediate, accurate list. You don't have to check each bot in the UI anymore.

Build a Self-Documenting MCP Server

Use the `create_bot` and `upload_data_source` tools to provision new bots programmatically. The real advantage here is that LlamaIndex can automatically record these actions and their results. Your knowledge base stays current with every API call from this MCP server. When you create a new bot, its configuration is immediately available for querying. You're not just running commands; you're building a system that documents itself.

Setup guide

Set up ChatFly 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 ChatFly 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 ChatFly tools.",
)
response = await agent.run("List recent ChatFly data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ChatFly. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ChatFly MCP in LlamaIndex

You use the ChatFly tools like `list_bots` and `get_bot` to fetch the current state of your chatbots. LlamaIndex then indexes this API data, letting your RAG application answer questions about your bot configurations with up-to-date information.

A great starting point is to build a dashboard. Use the tools to get data on all your bots and their knowledge sources, index it with LlamaIndex, and create a simple UI to ask questions like "how many bots do I have?" or "which bots use the 'support-policy.pdf' source?"

Yes. While LlamaIndex is great for querying, the agent can also use tools that change things. You can build an agent that finds a bot needing an update and then uses the `update_bot` or `upload_data_source` tool to perform the fix.

This server provides tools to manage the full lifecycle of your bots. You get `create_bot`, `get_bot`, `list_bots`, `update_bot`, `list_data_sources`, `upload_data_source`, and `chat`.

Your LlamaIndex application's access is scoped. When you index ChatFly bot configurations, that data lives only within your vector store and the secure Vinkius sandbox where the tool calls happen. Nothing is shared or persists on the Vinkius side after the job is done.

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