How to Use the Knack MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK read custom Knack object structures?

Yes. The agent calls listobjectfields to inspect your custom fields. It reads the exact schema before attempting to write anything.

Q: Will the OpenAI Agents SDK accidentally delete my Knack records?

It depends on how you configure your guardrails. The deleterecord tool is exposed, but you can restrict it to specific admin agents or require human-in-the-loop approval before execution.

Connect your OpenAI Agents SDK directly to your Knack databases to read schemas, filter records, and write data autonomously.

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Connect Knack MCP to OpenAI Agents SDK

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

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Read and map dynamic schemas

The `list_objects` and `get_object_schema` tools map out your Knack database structure before your agent attempts any writes. When your agent needs to sync customer profiles, it pulls the exact field keys directly from the source. You don't have to hardcode field mappings in your Python scripts. The agent uses `list_object_fields` to check data types dynamically. If someone changes a column name in the UI, your code adapts immediately without breaking the production pipeline.

Execute precise CRUD operations via MCP Server

The `create_record` tool pushes new JSON payloads directly into your Knack tables through this MCP Server. Your specialized data-entry agents format the exact JSON string required by the API, matching the field keys they just discovered. For existing entries, `update_record` handles partial updates so you only touch the fields that actually changed. If a record needs removal, you handoff the task to an admin agent to execute `delete_record`. This triggers OpenAI's built-in guardrails to pause and ask for human confirmation before wiping data.

Query and audit specific entries

The `search_records` tool accepts Knack Filter format JSON to find exact matches across thousands of rows. When a support agent needs context, it filters by email or status and pulls only the relevant entries into the context window. Once you have the record ID, `get_record` fetches the complete row for detailed auditing. Your agent passes this raw data back to the OpenAI dashboard. You get full visibility into exactly what the model read before making a decision.

Setup guide

Set up Knack 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 Knack tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Knack 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="Knack Agent",
            instructions="You have access to Knack 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 Knack MCP in OpenAI Agents SDK

Use the MCPServerStreamableHttp class with your endpoint URL. Pass it into the mcp_servers list when initializing your Agent. Set cacheToolsList=True to speed up initialization.

Yes. The agent calls list_object_fields to inspect your custom fields. It reads the exact schema before attempting to write anything.

The MCP Server executes requests as fast as your agent sends them. If you run a high-volume sync, you should implement a queue or rate-limiting step in your Python code to avoid HTTP 429 errors.

It depends on how you configure your guardrails. The delete_record tool is exposed, but you can restrict it to specific admin agents or require human-in-the-loop approval before execution.

This server processes your raw Knack database rows and field schemas. The data flows directly from the Knack API through the MCP connection to your OpenAI environment, without any persistent storage in the middleware.

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