How to Use the CatAAS MCP in AutoGen

Q: How do AutoGen agents negotiate which CatAAS tag to use?

One agent queries listtags to get the list of valid options, then proposes a tag to the group. The other agents review the proposal and approve it before calling getcatbytag.

Q: How do I register CatAAS tools from the MCP Server with an AutoGen AssistantAgent?

Use the mcpservertools helper to fetch the tools from the CatAAS MCP Server, then pass the resulting tool list directly into the AssistantAgent constructor.

Q: Can AutoGen agents handle animated GIFs from CatAAS?

Yes, the getrandomgifcat tool returns a URL that the agents can share in their chat context, allowing downstream agents to inspect or log the animated asset.

Let your AutoGen agents debate and select the perfect cat memes using CatAAS tools.

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Multi-agent debate over CatAAS tags in AutoGen

Set up a design agent and a copywriter agent in AutoGen to negotiate which cat tag fits a specific campaign. The design agent calls `list_tags` to find options, while the copywriter agent evaluates them. Once they agree on a tag, they call `get_cat_by_tag` to fetch the visual. This consensus-driven workflow ensures that the final cat image matches both aesthetic and copy requirements.

Collaborative meme critique using AutoGen agents

One AutoGen agent generates a meme using `get_cat_with_text`, while a critic agent reviews the output. The critic agent can request changes, prompting a new call to the MCP tool `get_cat_with_tag_and_text` with updated parameters. This collaborative loop continues until both agents reach consensus, delivering a polished cat meme without any human intervention in the loop.

Automated GIF curation via agent negotiation

Deploy an agent to pull random animations using `get_random_gif_cat` and another to filter them based on strict criteria. The filtering agent checks the metadata returned by `get_random_cat` to ensure quality. By dividing the labor, your AutoGen conversational group can curate high-quality cat content pipelines autonomously, handling API limits and tag validation through peer review over the MCP connection.

Setup guide

Set up CatAAS 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 CatAAS 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="CatAAS_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="CatAAS_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CatAAS. 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 CatAAS MCP in AutoGen

One agent queries `list_tags` to get the list of valid options, then proposes a tag to the group. The other agents review the proposal and approve it before calling `get_cat_by_tag`.

Agents can debate the text overlay in their conversation history, but once they reach consensus, a single agent invokes `get_cat_with_text` to render the final image.

Use the `mcp_server_tools` helper to fetch the tools from the CatAAS MCP Server, then pass the resulting tool list directly into the `AssistantAgent` constructor.

Yes, the `get_random_gif_cat` tool returns a URL that the agents can share in their chat context, allowing downstream agents to inspect or log the animated asset.

No, the CatAAS MCP Server only receives the specific arguments passed to tools like `list_cats`. The actual chat logs, agent debates, and internal prompts are never exposed to the external API.

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