How to Use the Cohere (Embed & Rerank) MCP in AutoGen

Enable multi-agent debate in AutoGen with Cohere (Embed & Rerank) to reach consensus on complex data tasks.

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Connect Cohere (Embed & Rerank) MCP to AutoGen

Create your Vinkius account to connect Cohere (Embed & Rerank) 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 (Embed & Rerank) with AutoGen

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Perplexity

Debate-driven embedding in AutoGen

Let your agents use `embed_texts` to provide evidence for their arguments. Other agents can compare these vectors to verify if a claim holds water. Use `rerank_documents` to help agents reach a consensus on which information is most relevant. It forces the team to focus on the strongest evidence during their deliberation.

Agent-negotiated classification in AutoGen

Deploy `classify_texts` so your agents can categorize data points as part of a joint decision. If agents disagree, they compare their classifications and debate the logic. Execute `chat_completion` to have your agents draft the final summary of their discussion. The tool ensures the output follows the expected conversational format.

Internal model management for AutoGen

Call `list_models` so your agents know which capabilities are available for the current task. They can adjust their strategy based on the available model endpoints. Use `tokenize_text` to ensure agents aren't overloading their messages. It helps the team stay within limits during intense back-and-forth negotiations.

Setup guide

Set up Cohere (Embed & Rerank) 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 (Embed & Rerank) 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 (Embed & Rerank)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Cohere (Embed & Rerank) data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Cohere (Embed & Rerank) MCP in AutoGen

Yes, agents can pass the output of one tool call as an argument to another. This allows the team to build on each other's findings.

Use the MCP adapter to convert the server tools into a list. You then pass this list to your AssistantAgent constructor to grant them access.

They can. By providing agents with the ability to rerank or classify data, you allow them to challenge each other's conclusions based on the tool results.

It does. You can initialize the server using the StreamableHttpServerParams to connect your agents over standard HTTP.

We use an ephemeral sandbox for all processing. Your text strings are cleared from memory the moment the agent receives the tool output.

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