Chainlit MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Chainlit 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 Chainlit. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Chainlit?"
)
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 Chainlit MCP Server
Connect your Chainlit Cloud projects to any AI agent and embrace a new paradigm of conversational observability. Analyze your AI app traffic directly from your terminal or chat.
LlamaIndex agents combine Chainlit tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Project Analytics — Trigger detailed data fetches mapping global traffic statistics, distinct user adoptions, and absolute utilization figures across your AI portfolio.
- Thread Introspection — Query explicit interaction boundaries isolating full chronological conversations from users securely and swiftly.
- Trace Logic Steps — Extrapolate internal logic jumps identifying explicit prompts, outputs, tool executions, and retrieval boundaries used per interaction.
- Qualitative Feedback — Automatically extract lists capturing precise thumbs up/down, implicit ratings, and explicit textual user reviews targeting your bot responses.
The Chainlit MCP Server exposes 6 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 Chainlit to LlamaIndex via MCP
Follow these steps to integrate the Chainlit 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 6 tools from Chainlit
Why Use LlamaIndex with the Chainlit MCP Server
LlamaIndex provides unique advantages when paired with Chainlit through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Chainlit tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Chainlit tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Chainlit, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Chainlit tools were called, what data was returned, and how it influenced the final answer
Chainlit + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Chainlit MCP Server delivers measurable value.
Hybrid search: combine Chainlit real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Chainlit 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 Chainlit for fresh data
Analytical workflows: chain Chainlit queries with LlamaIndex's data connectors to build multi-source analytical reports
Chainlit MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Chainlit to LlamaIndex via MCP:
get_stats
Retrieve explicit analytics statistics representing traffic boundaries and resource consumptions over native projects
get_thread
Retrieve the exact payload for a specific conversational thread locating exact node topologies
list_feedbacks
List absolute user review feedbacks rating explicitly conversational accuracy and value across deployments
list_projects
List explicit globally configured Chainlit Cloud projects managing independent app tracking spaces
list_steps
List raw programmatic interaction steps explicitly defining prompts and generations inside a single thread
list_threads
List conversational threads identifying user interaction boundaries inside a specific deployed project
Example Prompts for Chainlit in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Chainlit immediately.
"Retrieve the analytics stats of my currently enabled Chainlit cloud project targeting traffic."
"Search my cloud instance for the recent recorded chat interactions (threads) to fetch what clients asked today."
"Gather all negative feedbacks users submitted across this AI project."
Troubleshooting Chainlit MCP Server with LlamaIndex
Common issues when connecting Chainlit to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpChainlit + LlamaIndex FAQ
Common questions about integrating Chainlit 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 Chainlit 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 Chainlit to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.