How to Use the PandaDoc MCP in AutoGen

Q: Can I use PandaDoc contacts in AutoGen?

Yes, use listcontacts to find recipient details. Your agents can then verify contact info before initiating a signing request.

Let your AutoGen agents debate contract terms. Use this MCP Server to execute document actions based on team consensus.

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Connect PandaDoc MCP to AutoGen

Create your Vinkius account to connect PandaDoc to AutoGen 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 PandaDoc with AutoGen

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Debate contract terms with AutoGen

Your agents use `create_document` to draft proposals based on discussed terms. If one agent flags a clause, the team iterates before final generation. This ensures every document is vetted before it hits the API. You get a consensus-driven approach to contract preparation.

Validate PandaDoc signatures in AutoGen

The security agent calls `get_document_details` to verify signatures. If the status is incomplete, it triggers a reminder flow for the user. Your agents coordinate the entire signing process. They ensure no deal stalls due to missing signatures or forgotten steps.

Organize files using AutoGen agents

Agents use `list_folders` to scan your account structure. They categorize new contracts into the correct locations based on project metadata. This keeps your account tidy without manual sorting. Your agents manage the organization logic automatically.

Setup guide

Set up PandaDoc MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes PandaDoc tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="PandaDoc_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent PandaDoc data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="PandaDoc_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent PandaDoc data")
    print(result.messages[-1].content)

Get your connection token →

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