Vespper Python SDK

Open a Vespper document session and patch a supported MCP client so its normal tool APIs automatically carry Vespper metadata and retain updated DOCX bytes.

OpenAI Agents SDK

import asyncio
from pathlib import Path

from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
from vespper import Vespper


async def main() -> None:
    client = Vespper()
    session_id = await client.open_session("sample.docx")
    mcp = MCPServerStreamableHttp(
        name="Vespper DOCX",
        params={
            "url": client.mcp_url,
            "headers": {"Authorization": client.authorization_header},
        },
        cache_tools_list=True,
    )

    try:
        async with mcp:
            await client.patch_mcp_tools(
                mcp=mcp,
                session_id=session_id,
            )
            agent = Agent(
                name="DOCX Editor",
                instructions=(
                    "Use the available tools to read and edit the loaded document."
                ),
                model="gpt-5.5",
                mcp_servers=[mcp],
            )
            await Runner.run(agent, "Append the word hello to the document.")

        Path("sample-redlined.docx").write_bytes(
            client.get_session_document(session_id)
        )
    finally:
        await client.close_session(session_id)
        await client.close()


asyncio.run(main())

Native MCP and OpenAI

import asyncio
import json
from pathlib import Path

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from openai import OpenAI
from vespper import Vespper


async def main() -> None:
    vespper = Vespper()
    openai = OpenAI()
    session_id = await vespper.open_session("sample.docx")

    try:
        async with streamablehttp_client(
            vespper.mcp_url,
            headers={"Authorization": vespper.authorization_header},
        ) as (read, write, _):
            async with ClientSession(read, write) as mcp:
                await mcp.initialize()
                await vespper.patch_mcp_tools(
                    mcp=mcp,
                    session_id=session_id,
                )
                listed = await mcp.list_tools()
                tools = [
                    {
                        "type": "function",
                        "name": tool.name,
                        "description": tool.description,
                        "parameters": tool.inputSchema,
                    }
                    for tool in listed.tools
                ]
                input = [
                    {
                        "role": "user",
                        "content": "Append the word hello to the document.",
                    }
                ]

                for _step in range(6):
                    response = openai.responses.create(
                        model="gpt-5.5",
                        instructions=(
                            "Use the available tools to read and edit the loaded "
                            "document."
                        ),
                        tools=tools,
                        input=input,
                    )
                    input.extend(response.output)
                    calls = [
                        item for item in response.output if item.type == "function_call"
                    ]
                    if not calls:
                        break

                    for call in calls:
                        result = await mcp.call_tool(
                            call.name,
                            json.loads(call.arguments),
                        )
                        data = result.structuredContent or {}
                        input.append(
                            {
                                "type": "function_call_output",
                                "call_id": call.call_id,
                                "output": (
                                    data.get("message")
                                    if call.name == "edit_document"
                                    else json.dumps(data)
                                ),
                            }
                        )

        Path("sample-redlined.docx").write_bytes(
            vespper.get_session_document(session_id)
        )
    finally:
        await vespper.close_session(session_id)
        await vespper.close()


asyncio.run(main())