How to Use the CHATFLY MCP in LlamaIndex

Q: How do I filter CHATFLY data in LlamaIndex?

You use the allowedtools filter during client setup. This limits the agent to only the data sources you choose.

Index your CHATFLY knowledge base into LlamaIndex for grounded, accurate RAG responses.

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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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Ground LlamaIndex queries with CHATFLY

Use `list_uploaded_documents` to pull your raw support files and feed them into your LlamaIndex vector store. Your agents answer questions based on your actual documentation instead of guessing. Update your index whenever you run `trigger_bot_training` to ensure the model reflects the latest policy changes. You create a tight loop between your source files and your query engine.

Search CHATFLY history with LlamaIndex

Turn your `get_conversation_history` output into a searchable knowledge base that LlamaIndex can query. You find patterns in past support tickets by querying your index for specific customer issues. Connect historical logs to your retrieval pipeline to give your agents access to decades of support context. It turns static logs into a dynamic, queryable asset for your AI.

Automate LlamaIndex data ingestion

Pull account and bot details using `get_chatfly_account_info` and `list_chatfly_bots` to keep your metadata index current. Your agent knows exactly which bots are active and what their configuration limits are. This MCP server provides the structural data your index needs to map conversations to the right chatbot. You spend less time updating configs and more time refining your retrieval logic.

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

Use the MCP tool spec to wrap the server, then convert it to a tool list. You pass these tools to your FunctionAgent to enable RAG-based support.

The tools provide the raw data, and LlamaIndex handles the vectorization. You can index both uploaded documents and conversation history.

It is built for it. You pull the data, index it, and your agents use the retrieved context to answer support queries accurately.

You use the allowed_tools filter during client setup. This limits the agent to only the data sources you choose.

Your data stays within your local index once retrieved by the server. We keep API access limited to your ephemeral token, so no raw account credentials ever reach your vector store.

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