How to Use the Lancerkit MCP in OpenAI Agents SDK

Q: How do I connect Lancerkit to the OpenAI Agents SDK?

Install the package with pip install openai-agents. Initialize the connection using MCPServerStreamableHttp with your Vinkius URL and pass it to your agent's mcpservers array.

Q: Can my OpenAI agent read specific Lancerkit project details?

Yes, it uses the getproject tool to pull metadata for a single project ID. The agent auto-discovers this tool as soon as you connect the MCP server.

Q: How do I cache tool discovery for Lancerkit in OpenAI Agents SDK?

Set cacheToolsList=True when configuring the server parameters. This skips the initial tool discovery phase on subsequent runs and speeds up execution.

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Connect Lancerkit MCP to OpenAI Agents SDK

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

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Automate Lancerkit Invoicing via MCP Server

Using `list_invoices` lets your OpenAI agent pull global pipeline statistics directly into your Python application via this MCP Server. You define strict guardrails before execution, ensuring the agent never misinterprets critical financial data. The agent then runs `get_invoice` to extract specific payment histories and line items. Everything tracks through the OpenAI dashboard, giving you a complete audit trail of exactly when the system touched your client billing records.

Map Agency Workloads

The `list_projects` tool feeds your agent the exact standard project structures active in your workspace. You pass the Vinkius endpoint token, set `cacheToolsList=True` for speed, and your agent starts reading workload distribution instantly. Complex systems need specialized handoffs, so you build one agent to monitor `get_project` details and another to parse `list_tasks`. They communicate natively through the SDK, coordinating daily developer assignments without human intervention.

Audit Billable Hours

Calling `get_time_logs` gives your agent the raw hours spent on every task. It cross-references those hours against the billable items from `list_services` to calculate exact revenue totals. You can also run `get_client` to pull metadata for specific accounts. The agent checks the workspace connection using `get_status` first, confirming the Lancerkit link is active before it starts crunching numbers.

Setup guide

Set up Lancerkit 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 Lancerkit tools at runtime.
3

Create your Agent

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

Run the agent

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

Install the package with `pip install openai-agents`. Initialize the connection using `MCPServerStreamableHttp` with your Vinkius URL and pass it to your agent's `mcp_servers` array.

Yes, it uses the `get_project` tool to pull metadata for a single project ID. The agent auto-discovers this tool as soon as you connect the MCP server.

Direct APIs require custom glue code and manual schema mapping. The MCP protocol handles the translation automatically, letting your agent understand the exact required inputs for tools like `list_clients`.

Set `cacheToolsList=True` when configuring the server parameters. This skips the initial tool discovery phase on subsequent runs and speeds up execution.

This server processes raw payment records and billable service items. Vinkius runs the connection inside a V8 Isolate Sandbox, meaning your endpoint token handles authentication ephemerally and destroys the environment right after the request finishes.

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