CHATFLY MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
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.
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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 CHATFLY. "
"You have 8 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 workflows through natural conversation. Train and monitor your own AI agents using your business data.
LlamaIndex agents combine CHATFLY tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Chatbot Oversight — List and retrieve details for all custom AI chatbots in your account natively
- Knowledge Logistics — List all uploaded documents and data sources used for bot training flawlessly
- Training Automation — Trigger the training process for your chatbots to ingest new data securely
- Conversation Intelligence — Access recent chat conversations and full message history flawlessly
- Live Messaging — Send messages to your chatbots and receive AI-generated responses in real-time
- System Monitoring — Retrieve core account information and monitor your AI usage quotas directly within your workspace
The CHATFLY MCP Server exposes 8 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 CHATFLY to LlamaIndex via MCP
Follow these steps to integrate the CHATFLY 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 8 tools from CHATFLY
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
CHATFLY MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect CHATFLY to LlamaIndex via MCP:
get_chatbot_details
Get detailed information for a specific chatbot
get_chatfly_account_info
Retrieve core account and quota information
get_conversation_history
Retrieve the message history for a specific conversation
list_chatfly_bots
List all AI chatbots in your account
list_fly_conversations
List recent chat conversations
list_uploaded_documents
List all files uploaded to the knowledge base
send_bot_message
Send a message to a chatbot and receive a response
trigger_bot_training
Trigger the training process for a chatbot
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 active chatbots in CHATFLY."
"Show me the last 5 conversations for bot 'Support Assistant'."
"Send a test message to bot ID 123: 'How do I reset my password?'"
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.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect CHATFLY 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 CHATFLY to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.