How to Use the KnoCommerce MCP in OpenAI Agents SDK

Q: How do I connect KnoCommerce to my OpenAI Agents SDK project?

Install the package using pip, then use the MCPServerStreamableHttp class to point to your Vinkius MCP Server endpoint. Pass this server instance directly in the mcpservers list when initializing your agent.

Q: Can my OpenAI Agents SDK system handle large volumes of KnoCommerce survey responses?

Yes. Use listsurveyresponses to fetch the data in batches. Make sure to set cacheToolsList=True in your SDK configuration to keep agent execution fast and avoid repetitive schema lookups.

Q: How do I verify that the KnoCommerce API is online within my OpenAI Agents SDK workflow?

Your agent runs checkknocommerceapistatus at the start of a run. If the connection fails, the SDK's tracing dashboard logs the error, allowing your system to pause before running downstream tasks.

Q: How do I target a specific post-purchase campaign?

Run listsurveys to get a list of all active campaigns along with their unique identifiers. Pass the target identifier to listsurveyresponses to isolate feedback from that specific campaign using this MCP Server.

Feed real-time post-purchase survey data straight to your OpenAI Agents SDK workflows with zero-config tool discovery.

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Connect KnoCommerce MCP to OpenAI Agents SDK

Create your Vinkius account to connect KnoCommerce 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 KnoCommerce with OpenAI Agents SDK

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Audit buyer feedback profiles using OpenAI Agents SDK

The `get_customer_feedback_profile` tool pulls raw customer survey responses directly into your agent's context window. Your agent can run sentiment analysis or flag unhappy buyers without you writing any custom parsing code. By pairing this with `list_knocommerce_customers`, your OpenAI Agents SDK setup maps survey responses to specific buyer records. The agent handles the data lookup, letting you focus on writing clean prompt logic.

Map attribution reports to ad spend

The `get_channel_attribution_report` tool pulls post-purchase attribution metrics straight from your store. Your agent uses this raw data to spot which marketing channels actually drive customer acquisition. Feed these reports directly to your multi-agent system to compare self-reported attribution against your ad spend. The OpenAI Agents SDK manages agent handoffs so one agent pulls the data and another handles the budget math.

Inspect survey structures dynamically

The `list_survey_questions` tool exposes the exact structure of your active post-purchase questionnaires. This lets your agent inspect the data schema before attempting to parse individual answers. Your agent calls `get_survey_details` to check if a specific questionnaire is active. This prevents runtime errors in your OpenAI Agents SDK pipeline by verifying the survey state first using this MCP Server.

Setup guide

Set up KnoCommerce 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 KnoCommerce tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate KnoCommerce 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="KnoCommerce Agent",
            instructions="You have access to KnoCommerce tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 KnoCommerce. 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 KnoCommerce MCP in OpenAI Agents SDK

Install the package using pip, then use the `MCPServerStreamableHttp` class to point to your Vinkius MCP Server endpoint. Pass this server instance directly in the `mcp_servers` list when initializing your agent.

Yes. Use `list_survey_responses` to fetch the data in batches. Make sure to set `cacheToolsList=True` in your SDK configuration to keep agent execution fast and avoid repetitive schema lookups.

Your agent runs `check_knocommerce_api_status` at the start of a run. If the connection fails, the SDK's tracing dashboard logs the error, allowing your system to pause before running downstream tasks.

Run `list_surveys` to get a list of all active campaigns along with their unique identifiers. Pass the target identifier to `list_survey_responses` to isolate feedback from that specific campaign using this MCP Server.

The MCP Server runs inside a secure, ephemeral V8 sandbox that processes customer profiles and survey feedback without storing them. Your OpenAI Agents SDK system only receives the raw JSON response payload over an encrypted HTTPS connection.

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