Pull fields out of invoice text, match it to purchase orders and flag fraud risk. Connect ChatGPT and OpenAI Agents to the WingMan MCP gateway once and these tools are available in every conversation.
With the OpenAI Agents SDK (Python), connect over Streamable HTTP as below. The same URL works as a remote MCP tool in the Responses API and as a custom connector in ChatGPT developer mode.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
async with MCPServerStreamableHttp(name="wingman", params={"url": "https://wingmanprotocol.com/mcp", "headers": {"X-Wingman-Tools": "full"}}) as wingman:
agent = Agent(name="Assistant", mcp_servers=[wingman])
result = await Runner.run(agent, "Use the wingman tools for this task.")
print(result.final_output)
asyncio.run(main())
# Responses API instead of the SDK:
# tools=[{"type": "mcp", "server_label": "wingman", "server_url": "https://wingmanprotocol.com/mcp", "require_approval": "never"}]Names and descriptions come straight from the live tool registry.
invoice_extractExtract structured fields from raw invoice TEXT (vendor, number, dates, line items, totals).invoice_matchThree-way match an invoice against a list of purchase orders (amount %-diff + line-item similarity → best open PO, discrepancies, auto-appr…invoice_fraud_checkRule-based fraud risk for an invoice (duplicate / vendor-anomaly / round-amount / rush-terms) → score 0-100 + LOW/MEDIUM/HIGH/CRITICAL + re…These tools sit outside the default tool list, so the config above sets X-Wingman-Tools: full to load the whole catalogue. Without it, your agent can still reach them with discover_tools, then get_tool_schema, then tools/call.
Give ChatGPT and OpenAI Agents a task that needs it, for example “use invoice_extract from wingman”. The tool call goes to https://wingmanprotocol.com/mcp and the result comes back into the conversation.
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