CometChat MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CometChat 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 MCP SERVER
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 CometChat. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in CometChat?"
)
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 CometChat MCP Server
Connect your AI assistant to CometChat, the communication platform providing chat, voice, and video capabilities for applications.
LlamaIndex agents combine CometChat tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- User Management — List registered users, check online status, and retrieve user profiles.
- Group Operations — Create, list, and manage chat groups with different privacy settings.
- Message History — Retrieve conversation histories for users or groups and search past messages.
The CometChat MCP Server exposes 10 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 CometChat to LlamaIndex via MCP
Follow these steps to integrate the CometChat 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 10 tools from CometChat
Why Use LlamaIndex with the CometChat MCP Server
LlamaIndex provides unique advantages when paired with CometChat through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine CometChat tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain CometChat tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query CometChat, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what CometChat tools were called, what data was returned, and how it influenced the final answer
CometChat + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the CometChat MCP Server delivers measurable value.
Hybrid search: combine CometChat real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query CometChat 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 CometChat for fresh data
Analytical workflows: chain CometChat queries with LlamaIndex's data connectors to build multi-source analytical reports
CometChat MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect CometChat to LlamaIndex via MCP:
create_group
Create a new group in CometChat
create_user
Create a new user in CometChat
deactivate_user
Deactivate a user instead of deleting them
delete_user
Permanently delete a user from CometChat
get_group_details
Retrieve details of a specific group
get_user
Retrieve detailed information about a specific user
list_groups
Retrieve a list of groups from CometChat
list_messages
Retrieve chat history messages
list_users
Retrieve a list of users from CometChat
send_message
Send a text message to a user or group
Example Prompts for CometChat in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with CometChat immediately.
"Show me the first 20 users in CometChat."
"Create a public group named 'Marketing Team' with GUID 'marketing-team'."
"Retrieve the conversation history between users 'alice' and 'bob'."
Troubleshooting CometChat MCP Server with LlamaIndex
Common issues when connecting CometChat to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCometChat + LlamaIndex FAQ
Common questions about integrating CometChat 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 CometChat 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 CometChat to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.