How to Use the TextP2P MCP in AutoGen

Q: What if I need to add contacts before sending with AutoGen?

You simply incorporate the addtextp2pcontact tool call into the agent workflow. The agents will debate whether adding new contacts is worth the effort relative to the cost tracked by the MCP Server.

AutoGen MCP Client: Multi-Agent Consensus for P2P Outreach Strategy

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Setup TextP2P with AutoGen

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Debate Optimal TextP2P Messaging Formats

Build a team of agents with AutoGen, and let them debate the best strategy. One agent might argue that only `send_textp2p_sms` is fast enough for mass reach, while another pushes back, saying `send_textp2p_mms` offers necessary depth. The framework forces consensus by having agents challenge assumptions about which format—SMS or MMS—will actually move the needle.

Automated Contact List Validation via MCP Server

You can set up a process where one agent reviews your contact list using `list_textp2p_lists` and another agent debates if the contacts are compliant. The whole group iterates until they agree on a clean, actionable segment. This multi-agent approach handles complex pre-sending checks, moving beyond simple function calls to genuine decision-making.

Simulate Campaign Flow with TextP2P Tools

Need to test if a campaign works? AutoGen lets you model the entire process. One agent executes `send_textp2p_rvm` on a list, and another reports back potential issues like high bounce rates or budget concerns from `get_textp2p_balance`. The agents negotiate the final action plan based on simulated results, proving out your outreach strategy before you spend real credits.

Setup guide

Set up TextP2P 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 TextP2P 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="TextP2P_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent TextP2P 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="TextP2P_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent TextP2P data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TextP2P. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about TextP2P MCP in AutoGen

You can configure an agent specifically to monitor the output of `send_textp2p_sms`. When a rate limit is approached, that agent flags it for debate, forcing the team to slow down or change the sending method.

Yes. You can set up two opposing agents: one advocating for `send_textp2p_sms` and another arguing for `send_textp2p_rvm`. They will debate the pros and cons based on campaign goals.

You simply incorporate the `add_textp2p_contact` tool call into the agent workflow. The agents will debate whether adding new contacts is worth the effort relative to the cost tracked by the MCP Server.

It's built for it. You're not running a script; you're building a conversation where agents manage sequential steps, like checking balance, then listing contacts, and finally sending messages.

Since AutoGen involves multiple conversational roles, you must assign an agent role to specifically manage PII. This ensures that the rules around handling contact list details are debated and enforced before any action is taken.

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