How to Use the Cohere (AI Platform) MCP in AutoGen

Q: How do I register Cohere (AI Platform) tools with an AutoGen agent?

Install autogen-ext and use mcpservertools with the server's HTTP URL. Pass the resulting tool list directly into your AssistantAgent constructor, and the adapter will handle the schema mapping.

Enable multi-agent debate and consensus using Cohere (AI Platform) models in AutoGen.

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Connect Cohere (AI Platform) MCP to AutoGen

Create your Vinkius account to connect Cohere (AI Platform) 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 Cohere (AI Platform) with AutoGen

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Run multi-agent debate with AutoGen and Cohere (AI Platform)

Exposing `chat_generation` and `generate_text` as tools, this server gives your AutoGen agents the power to collaborate and debate. Different agents can generate opposing viewpoints and reach consensus using independent evaluations. One agent can draft a response while another reviews it, using the MCP Server to run independent evaluations. This setup ensures your multi-agent conversations stay grounded and highly structured.

Classify and route agent messages dynamically

Exposing `classify_inputs` as an MCP tool, this server routes AutoGen conversations to the right agent based on message content. Your coordinator agent can analyze user messages and assign the task to the specialized agent best suited for it. This keeps your AutoGen group chat organized. Instead of broadcasting every message to all agents, the system routes requests programmatically, saving API calls and reducing noise.

Embed and rank discussion history

Exposing `generate_embeddings` and `rerank_documents` as tools, this server keeps long-running AutoGen agent conversations coherent. When agents need to reference past decisions, they can search the conversation history or find the most relevant thread. This gives your agents a shared, searchable memory. They can also use `tokenize_text` to monitor the size of the conversation history, ensuring they don't hit model limits mid-debate.

Setup guide

Set up Cohere (AI Platform) 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 Cohere (AI Platform) 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="Cohere (AI Platform)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Cohere (AI Platform) 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="Cohere (AI Platform)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Cohere (AI Platform) 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 Cohere. 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 Cohere (AI Platform) MCP in AutoGen

Install autogen-ext and use mcp_server_tools with the server's HTTP URL. Pass the resulting tool list directly into your AssistantAgent constructor, and the adapter will handle the schema mapping.

Yes, the MCP Server supports concurrent requests, allowing different AutoGen agents to call tools like chat_generation or classify_inputs at the same time during a group chat.

The server supports both stdio and Streamable HTTP transports. For AutoGen setups, using the Streamable HTTP transport is recommended for clean, multi-agent network communication.

The McpToolAdapter automatically translates the server's tool definitions into the JSON schema format that AutoGen agents expect. This means tools like rerank_documents work out of the box without manual schema mapping.

All tool execution happens in an ephemeral, zero-trust V8 isolate. Your Cohere API keys and conversation transcripts are never logged or stored, ensuring total isolation for your multi-agent workflows.

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