Chainlit MCP Server for Mastra AI 6 tools — connect in under 2 minutes
Mastra AI is a TypeScript-native agent framework built for modern web stacks. Connect Chainlit through the Vinkius and Mastra agents discover all tools automatically — type-safe, streaming-ready, and deployable anywhere Node.js runs.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token — get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"chainlit": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "Chainlit Agent",
instructions:
"You help users interact with Chainlit " +
"using 6 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with Chainlit?"
);
console.log(result.text);
}
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.
Mastra's agent abstraction provides a clean separation between LLM logic and Chainlit tool infrastructure. Connect 6 tools through the Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution — deployable to any Node.js host in one command.
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 Mastra AI 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 Mastra AI via MCP
Follow these steps to integrate the Chainlit MCP Server with Mastra AI.
Install dependencies
Run npm install @mastra/core @mastra/mcp @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.ts and run with npx tsx agent.ts
Explore tools
Mastra discovers 6 tools from Chainlit via MCP
Why Use Mastra AI with the Chainlit MCP Server
Mastra AI provides unique advantages when paired with Chainlit through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure — add Chainlit without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every Chainlit tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host — Vercel, Railway, Fly.io, or your own infrastructure
Chainlit + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the Chainlit MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query Chainlit, process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed Chainlit as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query Chainlit on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using Chainlit tools alongside other MCP servers
Chainlit MCP Tools for Mastra AI (6)
These 6 tools become available when you connect Chainlit to Mastra AI 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 Mastra AI
Ready-to-use prompts you can give your Mastra AI 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 Mastra AI
Common issues when connecting Chainlit to Mastra AI through the Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcpChainlit + Mastra AI FAQ
Common questions about integrating Chainlit MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.Can Mastra agents use tools from multiple servers?
Does Mastra support workflow orchestration?
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 Mastra AI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.