How to Use the Chainlit MCP in AutoGen

Q: How do I configure the Chainlit MCP server for AutoGen?

Install autogen-extmcp via pip. Use mcpservertools with StreamableHttpServerParams to fetch the functions and pass them to your assistant agent.

Q: Can AutoGen agents discuss Chainlit thread data?

They do this natively. One agent calls listthreads to find interaction boundaries, then shares the results with the group for deliberation.

Q: Does the MCP adapter handle schema conversion?

The McpToolAdapter translates the parameters automatically. Your agents see native Python functions without worrying about the underlying JSON structures.

Give your AutoGen agents access to the Chainlit MCP Server so they can debate model performance and audit threads together.

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Connect Chainlit MCP to AutoGen

Create your Vinkius account to connect Chainlit to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Chainlit with AutoGen

Ask AI about this MCP

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Perplexity

Debate model performance with Chainlit MCP

The `get_stats` tool retrieves explicit analytics covering traffic boundaries and resource consumption. One agent pulls this data to argue for cost reductions based on high token usage. A competing performance agent reviews the same numbers to justify the current configuration. They negotiate the tradeoff between latency and accuracy before reaching a final recommendation.

Audit thread topologies collaboratively

Calling `get_thread` extracts the exact payload and node topologies for a specific interaction. A security agent inspects this structure for prompt injection attempts or boundary violations. Meanwhile, a quality assurance agent uses `list_steps` to read the raw programmatic prompts and generations. They discuss the findings and converge on a decision about whether the model behaved correctly.

Analyze user feedback and project health

Agents pull absolute user review ratings using the `list_feedbacks` tool. Your team of bots uses these explicit conversational accuracy scores to identify failing applications across deployments. If the ratings drop, they trigger `list_projects` to map out the globally configured spaces. The group then isolates the problematic app tracking space and flags it for human review.

Setup guide

Set up Chainlit MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Chainlit tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Chainlit_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Chainlit data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Chainlit_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Chainlit data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Chainlit. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Chainlit MCP in AutoGen

Install `autogen-ext[mcp]` via pip. Use `mcp_server_tools` with `StreamableHttpServerParams` to fetch the functions and pass them to your assistant agent.

They do this natively. One agent calls `list_threads` to find interaction boundaries, then shares the results with the group for deliberation.

You move past single-agent execution into consensus-driven analysis. Multiple agents challenge each other's interpretations of your project stats and feedback ratings.

The `McpToolAdapter` translates the parameters automatically. Your agents see native Python functions without worrying about the underlying JSON structures.

The zero-trust V8 Isolate Sandbox protects your data completely. Your explicit accuracy scores and raw programmatic steps never leak outside the ephemeral session, requiring a fresh endpoint token for every run.

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