How to Use the Cohere (AI Platform) MCP in OpenAI Agents SDK

Q: Can Cohere (AI Platform) tools prevent hallucinations in OpenAI Agents SDK?

Yes, by using rerankdocuments to force the agent to process specific, high-quality chunks. This limits the source material your agent considers, which keeps output grounded in your provided data.

Build production agents with Cohere (AI Platform) and OpenAI Agents SDK. Validate actions before they hit your infrastructure.

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Connect Cohere (AI Platform) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Cohere (AI Platform) to OpenAI Agents SDK 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 OpenAI Agents SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Run text generation and chat inside your agent

Call `chat_generation` or `generate_text` directly within your Python agent loop. Your code handles model responses as native objects while the OpenAI SDK manages the execution flow. These functions provide the raw output you need to drive logic. You aren't just getting strings back; you're getting structured data that fits right into your existing agent state.

Structure your context with Cohere (AI Platform) tools

Use `rerank_documents` to organize your search results before they reach your agent. This MCP server lets you filter noise so your agent only sees the most relevant data points. `generate_embeddings` creates the vectors you need for similarity searches. You'll find that having these tools available in your agent setup keeps your context window clean and focused.

Manage model availability through your agent

Check what is currently live with `list_models` to adjust your agent's behavior on the fly. Knowing which endpoints are active prevents runtime errors during your deployment cycle. `tokenize_text` helps you track usage before you send requests. It’s a simple way to monitor your token counts without guessing the model's math.

Setup guide

Set up Cohere (AI Platform) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Cohere (AI Platform) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Cohere (AI Platform) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Cohere (AI Platform) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Cohere (AI Platform) Agent",
            instructions="You have access to Cohere (AI Platform) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Cohere (AI Platform) MCP in OpenAI Agents SDK

Install the SDK and define an MCPServerStreamableHttp instance pointing to your Vinkius endpoint. Pass this server into your Agent constructor to let the SDK discover all seven tools automatically.

Yes, by using `rerank_documents` to force the agent to process specific, high-quality chunks. This limits the source material your agent considers, which keeps output grounded in your provided data.

The SDK catches exceptions from the MCP server during the tool call phase. You can set up custom handlers to retry or log specific failures before the agent proceeds.

Setting cacheToolsList=True minimizes overhead by caching the tool definitions. You get fast discovery without hitting the network every time the agent initializes.

Vinkius runs this in an isolated environment that clears your session state after every execution. Your text inputs are never stored permanently, ensuring that only the immediate task sees your proprietary data.

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