ChatFly MCP Server for LlamaIndexGive LlamaIndex instant access to 7 tools to Chat, Create Bot, Get Bot, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ChatFly as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The ChatFly app connector for LlamaIndex is a standout in the Customer Support category — giving your AI agent 7 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 ChatFly. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in ChatFly?"
)
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 ChatFly MCP Server
Connect your ChatFly account to any AI agent and take full control of your custom chatbot orchestration and automated knowledge ingestion workflows through natural conversation.
LlamaIndex agents combine ChatFly tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Bot Orchestration — Create and manage multiple high-fidelity AI chatbot instances programmatically, including configuring welcome messages and internal metadata
- Knowledge Ingestion — Programmatically train your bots by uploading website URLs and documents to coordinate an accurate, data-driven knowledge base
- Real-Time Interaction — Send messages and retrieve AI responses from specific bots to test performance or integrate chat into custom business applications
- Source Management — Access and monitor your complete directory of data sources (URLs, docs) to oversee the information feeding your digital assistants
- Operational Monitoring — Track chatbot performance, session histories, and account-level status directly through your agent for instant reporting
The ChatFly MCP Server exposes 7 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.
All 7 ChatFly tools available for LlamaIndex
When LlamaIndex connects to ChatFly through Vinkius, your AI agent gets direct access to every tool listed below — spanning chatbot-builder, conversational-ai, lead-qualification, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Interact with a chatbot
Provide name and welcome message. Create a new chatbot
Get details of a specific bot
List all chatbots
List data sources for a bot
Update an existing bot
Add a knowledge source to a bot
Connect ChatFly to LlamaIndex via MCP
Follow these steps to wire ChatFly into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the ChatFly MCP Server
LlamaIndex provides unique advantages when paired with ChatFly through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ChatFly tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ChatFly tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ChatFly, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ChatFly tools were called, what data was returned, and how it influenced the final answer
ChatFly + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ChatFly MCP Server delivers measurable value.
Hybrid search: combine ChatFly real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ChatFly 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 ChatFly for fresh data
Analytical workflows: chain ChatFly queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for ChatFly in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ChatFly immediately.
"List all my available chatbots in ChatFly."
"Train 'bot_1' by ingesting 'https://vinkius.com/faq'."
"Ask 'bot_1': 'What are your support hours?'."
Troubleshooting ChatFly MCP Server with LlamaIndex
Common issues when connecting ChatFly to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpChatFly + LlamaIndex FAQ
Common questions about integrating ChatFly MCP Server with LlamaIndex.