Runway, CAC to LTV, SaaS metrics and SAFE conversion from your own numbers. 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.
runwayMonths of runway and net monthly burn from cash on hand, revenue and expenses.cac_ltvCustomer acquisition cost, lifetime value, LTV:CAC ratio and payback months.saas_metricsEnding MRR, ARR, net new MRR, gross churn, net revenue retention and quick ratio.breakevenBreak-even units and revenue from fixed costs, unit price and variable cost.safe_noteConversion price and shares for a SAFE with a cap and/or discount.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 runway from wingman”. The tool call goes to https://wingmanprotocol.com/mcp and the result comes back into the conversation.
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