How to Use the Delivery Integrity Prover MCP in AutoGen

Q: How do AutoGen agents use Delivery Integrity Prover to reach consensus?

One agent calls verifydelivery to submit its work, while another reviews the generated file paths and test logs. They debate any gaps found until the tool returns a successful boolean verdict.

Q: How do I add the Delivery Integrity Prover tools to my AutoGen agents?

Install autogen-extmcp and use the mcpservertools helper to fetch the tools. Then, pass the tool list directly into your AssistantAgent constructor.

Make your AutoGen agents debate and prove task completion before they agree a job is done.

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Setup Delivery Integrity Prover with AutoGen

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Let agents debate task completion using `verify_delivery`

The `verify_delivery` tool provides the objective framework your AutoGen agents need to reach consensus, running directly through this MCP server. Instead of one agent blindly trusting another, the reviewer agent uses this tool to demand empirical validation logs and modified file paths. The developer agent must map every prompt requirement to an actual change. If the reviewer agent finds gaps in the boolean pivots, it rejects the delivery, forcing the developer agent to fix the code.

Enforce rigorous handoffs in AutoGen loops

The `verify_delivery` tool stops agents from passing half-baked work back and forth. By requiring a formal checklist of requirements and test outputs, it acts as a protocol that both agents must respect. This eliminates endless conversational loops where agents assume a task is complete without checking. They must present hard evidence of execution before the conversation is allowed to terminate.

Run secure validation inside this MCP Server

The `verify_delivery` tool gives your AutoGen coordinator agent a reliable way to audit the work of sub-agents. Before the coordinator marks the user's request as resolved, it runs this check to confirm all files were written correctly. This ensures that even complex, multi-step agent conversations result in verified, working code. You get a clean, structured verdict based on empirical test logs instead of polite agent agreement.

Setup guide

Set up Delivery Integrity Prover 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 Delivery Integrity Prover 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="Delivery Integrity Prover_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Delivery Integrity Prover 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="Delivery Integrity Prover_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Delivery Integrity Prover 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 Delivery Integrity Prover. 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 Delivery Integrity Prover MCP in AutoGen

One agent calls `verify_delivery` to submit its work, while another reviews the generated file paths and test logs. They debate any gaps found until the tool returns a successful boolean verdict.

Yes. If the critic agent detects that the validation logs or file paths do not match the prompt requirements, it triggers a rejection, forcing the writer agent to revise the work.

Install `autogen-ext[mcp]` and use the `mcp_server_tools` helper to fetch the tools. Then, pass the tool list directly into your `AssistantAgent` constructor.

Yes. The server handles concurrent validation requests, allowing multiple AutoGen agents to verify their respective tasks and file modifications simultaneously without conflict.

Your prompt requirements, file paths, and execution logs are processed in a zero-trust, ephemeral V8 isolate sandbox. No data is stored, keeping your MCP server inputs and multi-agent design discussions completely private.

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