How to Use the Canny MCP in AutoGen

Q: How do I configure Canny tools for AutoGen?

Use mcpservertools with StreamableHttpServerParams pointing to your Vinkius endpoint. Pass the resulting tool list directly into your AssistantAgent constructor.

Deploy AutoGen multi-agent systems that debate and manage Canny product feedback autonomously.

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Connect Canny MCP to AutoGen

Create your Vinkius account to connect Canny 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 Canny with AutoGen

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Connect AutoGen to the Canny MCP Server

Connecting AutoGen to Canny allows multiple agents to debate feature requests before touching your boards. They do not just execute commands blindly. You can assign one agent to advocate for user requests while another protects engineering bandwidth. A product agent might use `list_feedback_posts` to find highly requested features. An engineering agent reviews the list and argues against complex implementations. Once they reach consensus, a designated execution agent fires `create_feedback_post` or `vote_on_post` to reflect the agreed priority.

Triage bugs via consensus

Triaging Canny bugs via consensus prevents a single AI from making categorization mistakes. Bug reports often lack detail or duplicate existing issues. A multi-agent system cross-checks information before making a final decision. The first agent runs `list_comments` to gather context on a reported bug. A second agent verifies this against known issues using `get_post_details`. If they agree it is a new bug, they authorize a third agent to log it. The built-in McpToolAdapter handles all the schema conversions behind the scenes.

Analyze user sentiment

Analyzing Canny user sentiment requires processing thousands of interactions across multiple boards. Figuring out what users actually want takes time. You can build a specialized team of agents to break down this data and summarize the core problems. One agent loops through `list_feedback_boards` and assigns specific boards to worker agents. Those workers pull data via `list_votes` and `list_users` to see who is asking for what. The manager agent compiles their findings and uses `add_comment` to post an executive summary back to the internal tracking ticket.

Setup guide

Set up Canny 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 Canny 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="Canny_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Canny 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="Canny_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Canny 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 Canny. 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 Canny MCP in AutoGen

Use mcp_server_tools with StreamableHttpServerParams pointing to your Vinkius endpoint. Pass the resulting tool list directly into your AssistantAgent constructor.

Yes. You can provide the same Canny tool list to different agents in your group chat. They will coordinate who calls which tool during their debate.

The framework uses McpToolAdapter to automatically convert AutoGen function calls into the correct MCP schema. You do not need to write custom parsers for Canny inputs.

They can. If the agents reach a consensus that a feature is critical, the authorized agent executes `vote_on_post` to bump its score.

Vinkius isolates the connection in a V8 sandbox. Your agents process raw user feedback and voter emails in memory, and the connection drops immediately after the task completes.

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